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REVIEW article

Artificial intelligence in optical communications: from machine learning to deep learning.

• State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, China

Techniques from artificial intelligence have been widely applied in optical communication and networks, evolving from early machine learning (ML) to the recent deep learning (DL). This paper focuses on state-of-the-art DL algorithms and aims to highlight the contributions of DL to optical communications. Considering the characteristics of different DL algorithms and data types, we review multiple DL-enabled solutions to optical communication. First, a convolutional neural network (CNN) is used for image recognition and a recurrent neural network (RNN) is applied for sequential data analysis. A variety of functions can be achieved by the corresponding DL algorithms through processing the different image data and sequential data collected from optical communication. A data-driven channel modeling method is also proposed to replace the conventional block-based modeling method and improve the end-to-end learning performance. Additionally, a generative adversarial network (GAN) is introduced for data augmentation to expand the training dataset from rare experimental data. Finally, deep reinforcement learning (DRL) is applied to perform self-configuration and adaptive allocation for optical networks.

Introduction

Machine learning (ML) techniques have been developed and applied to optical communication in both the physical layer and network layer for years ( Musumeci et al., 2018 ; Khan et al., 2019 ). Various algorithms from ML communities powered a wide range of aspects in optical communication, involving digital signal processing (DSP), optical performance monitoring (OPM), signal detection and analysis, proactive fault management, network automation, and optical sensing, etc. The conventional ML system is limited by the ability to undertake feature extraction and complex analysis, and has always relied on considerable domain expertise and feature engineering. In recent years, rapid advances in information technology have made great strides and parallel developments in computation and low-cost computing hardware have made big data modeling possible. Driven by this growth in the volume of data and improvements to computing power, ML has successfully evolved into deep learning (DL), which addresses complex and large-scale problems with robust, adaptable, and efficient solutions ( LeCun et al., 2015 ), as illustrated in Figure 1 .

Figure 1 . Advances in artificial intelligence in optical communications. Driven by powerful parallel computing capacity and big data, traditional machine learning algorithms are progressing to deep learning techniques with a variety of applications, promoting the evolution of optical communications toward intelligence.

In general, DL can be understood as a deep neural network (DNN) with multiple non-linear layers made up of a large number of neurons, each of which is mathematically modeled as an activation function. In DL communities, different algorithms with specific structures were suitable for different problems and specialized in different data types. Among them, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), deep reinforcement learning (DRL), end-to-end learning based on autoencoder, and their variants have made a distinctive contribution to fields such as machine vision, natural language processing, drug discovery, genomics, speech recognition, information retrieval, affective computing, and automatic deriving ( Deng, 2014 ). Meanwhile, to promote the development of artificial intelligence (AI) in optical communication, the evolution from ML to DL is making major advances in a wide variety of applications in both physical and network layers ( Fan et al., 2020 ; Häger and Pfister, 2020 ; Saif et al., 2020 ).

This paper reports the progress of AI in optical communication from ML to DL. Unlike other review papers about conventional ML algorithms, the presentation focuses on state-of-the-art DL techniques and aims to highlight the contributions of DL to optical communication for both the physical layer and the network layer. Examining the characteristics of different DL algorithms and data types, we briefly review multiple DL-enabled applications for optical communication. First, as one of the most popular DL algorithms, CNN is introduced for image recognition to process seven kinds of common image data from optical communication to execute various functions. Then RNN is applied for sequential data analysis to process digital signal waveforms, network traffic data, and equipment state parameters. In addition, a data-driven channel modeling technique using DL is proposed to provide a supplementary solution to the conventional block-based modeling, which could also improve end-to-end learning performance. As an emerging technique, GAN is implemented for data augmentation to expand image data and network traffic data. Finally, DRL is considered for various decision-making tasks, including routing, resource allocation, and automatic configuration.

Convolutional Neural Network for Image Data

DL belongs to a branch of the ML family mainly referring to the faction of neural networks. The term “neural network” has its origins in attempts to find mathematical representations of information processing in biological systems, which are built of a lot of interconnected neurons. As the basic unit of a neural network, each neuron can be modeled as an activation function to emulate the process of transferring information in the practical biological system. According to the network topology, neural networks can be categorized into feedforward networks and feedback networks. A convolutional neural network is a specialized type of feedforward network for primarily processing image data that can be regarded as a two-dimension (2D) grid of pixels ( LeCun et al., 2015 ). The operating process of CNN can be described as convolution, pooling, and activation.

Convolution

The kernel convolves with pixel points across the width and height of the input image, computing the dot product between the entries of the kernel and input. The kernel works like a filter that scans the input image to extract the informative features for recognition. The extracted features from the image are displayable and explainable, such as eyes, nose, or mouth in face photos. Convolution takes advantage of sparse interaction, parameter sharing, and equivariant representations to improve the performance of image recognition.

Pooling: Down-Sampling Operation

The output of the convolution layer at a certain location is replaced by a summary statistic of the nearby outputs. The typical pooling is to calculate the average or maximum value of a small local region in one feature map to down-sample the dimension of the feature map, thereby greatly reducing the parameter size and creating an invariance to small translations of the input.

Activation: Non-linear Operation

The representation capacity of the whole network is improved through the non-linear mapping between adjacent layers. Common activation functions include ReLU, Softmax, Softplus, and Sigmoid, etc.

Due to the above factors, CNN is particularly effective at examining image data, including image recognition, objection detection, image understanding, and video translation ( Gu et al., 2018 ). It has been statistically established that images often account for a large proportion of various data types. Therefore, CNN is one of the most useful approaches in DL for image processing. In optical communication, most data are denoted in the format of a digital signal, while some other kinds of information are presented in the form of images, as summarized and displayed in Figure 2 . Compared with the data format of digital vectors, one great advantage of image formats is that various digital data of different sizes can be comprehensively and integrally presented in a picture with a fixed pixel size. Image data with a fixed size can therefore contain various information, which is important for ML and DL in keeping their structures stable ( Wang et al., 2019 ).

Figure 2 . Application of convolutional neural network (CNN) in optical communication for image processing. (A) Summarization of image data in optical communication: linear polarization (LP) mode diagrams, orbital angular momentum (OAM) mode diagrams, eye diagrams, constellation diagrams, asynchronous delay-tap plot (ADTP) diagrams, asynchronous amplitude histograms (AAH) diagrams, and optical spectrum diagrams. (B) The structure of CNN is composed of convolution layers, pooling layers, and fully-connected layers. (C) A variety of functions can be achieved by CNN for optical communication.

As can be seen from Figure 2 , the seven kinds of typical image data in optical communication are linear polarization (LP) mode diagrams, orbital angular momentum (OAM) mode diagrams, eye diagrams, constellation diagrams, optical spectrum diagrams, asynchronous amplitude histograms (AAH) diagrams, and asynchronous delay-tap plot (ADTP) diagrams (ADTP combines asynchronous sampling with a two-tap delay line, so that each sample point comprises two measurements, separated by a fixed time corresponding to the delay length. The samples are plotted as sample pairs, producing a joint map of the power and evolution over the delay time) ( Wang et al., 2017a , b ; Li et al., 2018 ). Through analyzing and processing these image data, CNN can explore a large number of informative features for optical communication to execute a variety of functions, including but not limited to channel estimation, mode demodulation, optical signal analysis, impairment diagnosis, OPM, DSP, and spectral analysis. For example, CNN is capable of: detecting mode crosstalk and estimating a few mode fiber channels from LP mode diagrams; demodulating multiplexed modes and detecting atmospheric turbulence from OAM mode diagrams; analyzing the signal quality; diagnosing system impairments from eye diagrams (for intensity-modulated signals) and constellation diagrams (for complex-modulated signals); monitoring optical-to-noise ratio (OSNR) and identifying modulation format with low-cost methods from ADTP and AAH diagrams; and measuring and analyzing spectral characteristics from spectrum diagrams.

Recurrent Neural Network for Sequential Data

Unlike CNN designed for image data, RNNs are specifically proposed for sequential data, where temporal correlations exist at a range of different timescales. Different from feedforward neural networks, RNNs containing cyclic connections aim to provide neural networks with memory, meaning that the outputs are not only related by the current inputs but also the formerly available information ( Mikolov et al., 2010 ). Thus, RNNs have achieved great success in sequence modeling and prediction tasks, such as speech recognition, handwriting recognition, language translation, and stock price forecasting. The principle of RNN is illustrated in Figure 3 . The input vector is a series of sequential data X = {… x t −1 , x t , x t +1 …}, and the neurons in the hidden layer get inputs from not only x t of the input layer but also the output h t −1 of the hidden layer at the previous time steps. Passing through multiple hidden layers, an input sequence x t can be mapped into an output sequence y t that involves some previous stated information.

Figure 3 . Application of recurrent neural network (RNN) in optical communication for sequential data processing. (A) Summarization of sequential data in optical communication: digital signal waveforms, network traffic data, and equipment state operating parameters. (B) The schematic of RNN considers the extracted features in the previous state as the one of the current input information and the current outputs depend on the current and previous inputs to provide the neural network with memory; and Long short-term memory (LSTM) block diagram is the variant of RNN that can learn long-range temporal relationships among sequential data. (C) A variety of functions can be achieved by RNN for optical communication.

However, conventional RNN finds it difficult to learn long-term dependencies from sequential data. To overcome this weakness in RNNs, long short-term memory (LSTM) was designed to learn long-range temporal relationships among sequential data and remember inputs for a long time ( Zia and Zahid, 2019 ). As one of the most famous RNN variants, the core idea of LSTM is the memory cell, which can pass information through time steps, and structures called gates, which are used to remove or add information to the memory cell, as shown in Figure 3B . The operating process of LSTM can be summarized by forgetting the old state and memorizing the fresh state such that the useful information in the cell can be passed on, and the useless information can be discarded. Thus, LSTM can not only allow the accumulation of information over a long period of time but also forgets the old state by setting it to zero and starting to count afresh.

In the era of big data, except for image data, most of the rest are sequential data, such as speech, language, and words. In optical communication, most data are sequential data, such as optical and electrical signals, network traffic data, equipment state operating parameters, as summarized and displayed in Figure 3A . In optical communication, for tasks that involve these sequential data, it is better to use RNNs to realize digital signal pre-distortion and post-compensation, inter-symbol interference (ISI) cancellation, network traffic prediction, and equipment failure management, etc.

The optical signals can be regarded as a series of time-domain data, and the mutual influence and the experienced impairments from the transmission process can be embodied into temporal signal waveforms. Considering the superior performance of RNN for these data, RNN can pre-distort signal before transmission to resist transmitter imperfection and the post-compensate signal after receiver to mitigate system impairments or identify the crosstalk between adjacent symbols to cancel the ISI ( Lu et al., 2019 ; Deligiannidis et al., 2020 ; Zhao et al., 2020 ).

For network traffic data, the traffic loads fluctuate regularly or irregularly over time according to daily statistics ( Lu et al., 2015 ). Based on previous scenes, RNN can build a prediction model for large-scale network traffic forecasting from the perspective of temporal analysis, which is important for load balancing and network planning ( Gui Y. et al., 2020 ; Zheng et al., 2020 ).

Early-warning and proactive protection are becoming increasingly critical for network operators as a failure of the optical network could result in huge economic loss. The operating conditions of network equipment can be reflected in the equipment state parameters, which are varied over time. Through analyzing a great deal of historical data, RNN can learn the variation trend of state parameters and establish a failure prediction mechanism to prevent risk in advance ( Wang et al., 2018 ; Zhang et al., 2020 ).

End-To-End Learning for Joint Optimization With Dl-Based Channel Model

The conventional model of the optical communication system is constructed in a divide-and-conquer manner and consists of a series of model blocks, including symbol mapping, shaping filter, laser, modulator, fiber channel, amplifier, optical filter, detector, low-pass filter, and digital sampling, as shown in Figure 4A . This block-based optical communication system is strongly dependent on practical channel conditions and is characterized by rigid mathematical models ( Agrawal, 2012 ). However, the conventional block-based communication systems still have the following deficiencies: (a) they are only effective in tractable and stable scenarios, but invalid for those complex and dynamic scenarios; (b) they require a lot of artificial expertise; and (c) they have a relatively long computation time owing to the small step sizes and repeated iterative operations they undertake.

Figure 4 . Deep learning for optical communication modeling. (A) The conventional block-based optical communication system, constructed in a divide-and-conquer manner using a series of model blocks. (B) Deep learning-based optical communication model, built by the data-driven multi-layer neural network. (C) Schematic of end-to-end learning for optical communication, based on the DL-based channel model.

In deep learning communities, autoencoder is another important and popular algorithm. It is an unsupervised learning algorithm for a neural network that sets the target output values to equal the inputs. The autoencoder has been applied in dimensionality reduction, feature reconstruction, and data encryption ( Tschannen et al., 2018 ). A new fundamental way to interpret the entire communication systems as an autoencoder has been proposed. It was first presented in wireless communication systems before being introduced to optical communication systems ( Karanov et al., 2018 ). This technique is based on the concept of end-to-end learning that seeks to jointly optimize the transmitter and receiver components in a single process. However, a major drawback hindering practical implementation is that a differentiable channel model is necessary to execute parameter adjustment through backpropagation. Accordingly, a DL-based fiber channel modeling scheme was proposed ( Wang et al., 2020 ). In theory, DL can approximate any function to solve both linear and non-linear problems. According to the characteristics of DL, the model functions can be approximated by mapping independent to dependent variables, corresponding to the input and output data as shown in Figure 4B . DL constructs an approximate model for a black box driven by source data and received data. Furthermore, because the scheme does not rely on expert experience, it can significantly reduce the modeling cost and improve the simulation efficiency. This transmission simulation model in the DT system can not only digitize the physical process but also provide the numerical channel model that is important for adaptive damage compensation, like the end-to-end learning method, to ensure high reliable transmission of optical communication. Based on the idea of an auxiliary channel, a DL-based channel as shown in Figure 4C was also flexibly embedded into an end-to-end learning model to perform joint optimization more accurately ( Karanov et al., 2020 ; Li M. et al., 2020 ).

Generative Adversarial Network for Data Augmentation

One of the main motivations for DL is having an effective and available dataset for training, and more adequate data contribute to a better generalization of the model. However, in practice, labeled data are valuable and rare. In optical communication, it is difficult to collect both image data and sequential data, particularly experimental data and practical data from network operators or corporations. In addition to guaranteeing sufficient data, diversity is also essential to improving the robustness and generalization of DL models. Therefore, a lack of sufficient and diverse training data is one of the major limitations on DL to be well-applied in optical communication.

GAN was recently introduced as an emerging technique to implement data augmentation. At first, GAN was proposed by Ian Goodfellow et al . as a way to generate image data, including handwritten digits, human faces, and animal images ( Goodfellow et al., 2014 ). The idea behind GAN was based on the concept of zero-sum game theory, as shown in Figure 5 . The framework of GAN consists of two neural network models: a generative model called generator captures the data distribution and output of the generated samples, and a discriminative model that distinguishes whether a sample came from the real dataset or a generated one. During the training procedure, the two models compete with each other. The generator is designed to generate data as realistic as possible so that it is difficult to distinguish them, while the discriminator as a binary classifier aims to identify real and fake data as accurately as possible. The generator and discriminator are optimized alternately until the augmented data are indistinguishable from the actual data.

Figure 5 . Schematic of the generative adversarial network, consisting of two neural networks: a generator and a discriminator. The generator is used to produce the approximated samples from the N -dimensional random noise. The discriminator is used to identify whether a sample is real or fake. These two networks compete with each other and are optimized gradually to realize data augmentation.

Inspired by GAN, a number of new applications have been discovered in terms of images, such as image synthesis, image style transfer, image-to-image translation, and image reconstruction ( Gui J. et al., 2020 ). For optical communication, except for image data, other data types can also be combined with GAN. A network traffic data augmentation technique using GAN was proposed to augment the traffic dataset adaptively for various scenarios ( Li J. et al., 2019 ; Li S. et al., 2019 ). Based on limited experimental traffic data, GAN captured distribution characteristics and then generated massive diverse traffic data, which significantly expanded the training dataset and improved the performance of DL models. Therefore, not limited to image data, GAN can be applied to arbitrary data types by designing appropriate generators for specific application requirements in optical communication.

Deep Reinforcement Learning for Network Automation

Reinforcement (RL) has made great breakthroughs in solving complicated controlling problems based on environment-aware mechanisms. DL plays an important role in perception that can acquire information from observation of the environment and provide current state information, while RL shows powerful advantages in decision-making that can sense complex system states and learn best policies through repeated interactions with the environment, as shown in Figure 6 . DRL combines the perception of DL and the decision of RL to learn a policy that maximizes the cumulative rewards for various tasks, like playing Go, competitive video games, controlling continuous systems in robotics.

Figure 6 . Schematic of deep reinforcement learning combing advantages of perception from deep learning and decision-making from reinforcement learning, to provide a policy for complex controlling problems. Through observation, an agent can acquire the current information from the environment and adjust the action to maximize cumulative rewards for a specific purpose.

The schematic of DRL is displayed in Figure 6 . It can be observed that in DRL there are two main elements (agent and environment) and two core steps (observation and action). The observation provides the current state information of the environment and the action represents the adjustment that the DRL agent makes according to the rewards or punishments from the environment. Therefore, DRL reflects a universal truth that the machine learns from failures in the past and grows after correcting them. Similarly, the agent of DRL learns from rewards and punishments rather than explicit instruction. Through repeated training and learning for a specific purpose, the agent grows powerful gradually to earn more rewards and avoid making mistakes, even exceeding human capacity in many domains.

In the context of optical communication, DRL is particularly useful for network control and automation and thus has been applied in the network layer to automatize the resolutions of routing, resource allocation, orchestration, and configuration ( Chen et al., 2019a , b ; Suárez-Varela et al., 2019 ; Andreoletti et al., 2020 ; Wang et al., 2021 ). A DRL-based routing solution was proposed for the optical transport network (OTN) that can better capture the crucial relationships among the lightpaths and paths in OTN topologies ( Suárez-Varela et al., 2019 ). Considering the real network topologies and traffic profiles, the routing policy learned by the agent outperformed well-known routing heuristics. Moreover, the elastics optical network (EON), where the spectrum distribution becomes extremely flexible and spectrum resource management confronts big challenges ( Yin et al., 2013 ; Zhu et al., 2013 ; Gong and Zhu, 2014 ), requires more automatic and smart control schemes. Accordingly, a DRL-based spectrum assignment scheme was introduced in A DRL-based observer to select the duration of each service cycle adaptively for realizing adaptive and high-quality virtual network function services ( Li B. et al., 2020 ). This study obtained superior results, especially under dynamic, flexible, and complex scenarios.

Additionally, we proposed an adaptive optical transceiver configuration technique using DRL for data center optical networks and passive optical networks ( Li J. et al., 2020 ). The traditional transceivers are only suitable for static scenarios, where the transmission capability is fixed and massive spectrum resources are wasted. Therefore, the flexible optical transceiver is considered as a promising candidate to realize flexible services provisioning but faces the challenges of searching for optimum transceiver parameter sets when considering complex network conditions, including diverse user types, modulation formats, multi-level access distances, quality of transmission, and transmission speed. With the help of DRL, flexible transceivers can be adaptively configured according to network environment states. To improve throughput and spectral efficiency, the agent gradually learns the relationship between network state and the reward of varied configuration actions.

Conclusions

In this paper, powerful DL algorithms were introduced in optical communication to achieve a variety of applications. CNN was used to explore information from image data, including LP mode, OAM mode, eye, constellation, ADTP, AAH, and spectrum diagrams, to implement channel estimation, mode demodulation, optical signal analysis, impairment diagnosis, OPM, DSP, and spectral analysis. RNN was applied to process sequential data, including digital signal waveform, network traffic data, and equipment state parameters, to execute signal pre-distortion and post-compensation, network traffic forecasting, and fault alarming analysis. A data-driven channel modeling scheme was proposed to rethink conventional modeling methods and improve end-to-end learning performance. GAN was adopted to augment image data and sequential data to ensure that the training data were sufficient and diverse. Finally, DRL was introduced to realize self-configuration and the adaptive allocation of optical networks. DL enables optical communication to be more intelligent and adaptive and is expected to make further contributions to optical communication in the near future.

Author Contributions

DW contributed to the study of convolutional neural networks and recurrent neural networks. MZ focused on reinforcement learning-related research.

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61975020 and 61871415), the Key Laboratory Fund (Grant No. 6142104190207), and the Fund of State Key Laboratory of IPOC (BUPT) (Grant No. IPOC2020ZT05), P. R. China.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: artificial intelligence, machine learning, deep learning, optical communications, optical networks

Citation: Wang D and Zhang M (2021) Artificial Intelligence in Optical Communications: From Machine Learning to Deep Learning. Front. Comms. Net. 2:656786. doi: 10.3389/frcmn.2021.656786

Received: 21 January 2021; Accepted: 08 March 2021; Published: 31 March 2021.

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Copyright © 2021 Wang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Danshi Wang, danshi_wang@bupt.edu.cn ; Min Zhang, mzhang@bupt.edu.cn

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Journal of Optical Communications

• Online ISSN: 2191-6322
• Print ISSN: 0173-4911
• Type: Journal
• Language: English
• Publisher: De Gruyter
• First published: January 1, 1980
• Publication Frequency: 4 Issues per Year
• Audience: Researchers and practitioners interested in all aspects of optical communications

Enhancing the performance and efficiency of optical communications through soliton solutions in birefringent fibers

• Research Article
• Published: 18 January 2024

Cite this article

• Salman A. AlQahtani 1 ,
• Mohamed E. M. Alngar   ORCID: orcid.org/0000-0002-5436-7268 2 ,
• Reham M. A. Shohib 3 &
• Abdulaziz M. Alawwad 4  

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This paper represents a comprehensive exploration of mathematical modeling and analysis in the context of fiber communications, with a specific focus on optimizing performance and efficiency. Positioned at the vanguard of innovation within the field of nonlinear optics, this research stands as a pioneering effort, venturing into uncharted territory by comprehensively examining soliton solutions. We direct our inquiry to the cubic-quartic nonlinear Schrödinger equation model, specifically as applied to birefringent fibers exhibiting cubic-quintic-septic-nonic (CQSN) nonlinearity. This study delivers a distinctive and noteworthy contribution to the scientific landscape. Employing advanced mathematical techniques, notably the generalized auxiliary equation technique, we have derived a diverse array of soliton solutions for CQ optical solitons within birefringent fibers. These solutions encompass dark, bright, singular, combo-bright-dark, and combo-singular solitons. Through the lens of the CQSN nonlinear Schrödinger’s equation, our investigation delves into the dynamic behavior of the system and its implications for fiber communication. This manuscript encapsulates original and innovative research, illuminating the potential of mathematical methodologies to enhance the design and operation of fiber communication systems. It underscores the pioneering essence of our study, emphasizing the practical significance of our findings in advancing the field of nonlinear optics and its potential impact on real-world applications.

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Acknowledgements

The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research. (IFKSURC-1-7106).

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New Emerging Technologies and 5 G Network and Beyond Research Chair, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

Salman A. AlQahtani

Basic Science Department, Faculty of Computers and Artificial Intelligence, Modern University for Technology & Information, Cairo, 11585, Egypt

Mohamed E. M. Alngar

Basic Science Department, Higher Institute of Foreign Trade & Management Sciences, New Cairo Academy, Cario, 379, Egypt

Reham M. A. Shohib

Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

Abdulaziz M. Alawwad

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AlQahtani, S.A., Alngar, M.E.M., Shohib, R.M.A. et al. Enhancing the performance and efficiency of optical communications through soliton solutions in birefringent fibers. J Opt (2024). https://doi.org/10.1007/s12596-023-01490-6

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Received : 22 September 2023

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Published : 18 January 2024

DOI : https://doi.org/10.1007/s12596-023-01490-6

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Nanostructures enable on-chip lightwave-electronic frequency mixer

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Imagine how a phone call works: Your voice is converted into electronic signals, shifted up to higher frequencies, transmitted over long distances, and then shifted back down so it can be heard clearly on the other end. The process enabling this shifting of signal frequencies is called frequency mixing, and it is essential for communication technologies like radio and Wi-Fi. Frequency mixers are vital components in many electronic devices and typically operate using frequencies that oscillate billions (GHz, gigahertz) to trillions (THz, terahertz) of times per second. 

Now imagine a frequency mixer that works at a quadrillion (PHz, petahertz) times per second — up to a million times faster. This frequency range corresponds to the oscillations of the electric and magnetic fields that make up light waves. Petahertz-frequency mixers would allow us to shift signals up to optical frequencies and then back down to more conventional electronic frequencies, enabling the transmission and processing of vastly larger amounts of information at many times higher speeds. This leap in speed isn’t just about doing things faster; it’s about enabling entirely new capabilities.

Lightwave electronics (or petahertz electronics) is an emerging field that aims to integrate optical and electronic systems at incredibly high speeds, leveraging the ultrafast oscillations of light fields. The key idea is to harness the electric field of light waves, which oscillate on sub-femtosecond (10 -15 seconds) timescales, to directly drive electronic processes. This allows for the processing and manipulation of information at speeds far beyond what is possible with current electronic technologies. In combination with other petahertz electronic circuitry, a petahertz electronic mixer would allow us to process and analyze vast amounts of information in real time and transfer larger amounts of data over the air at unprecedented speeds. The MIT team’s demonstration of a lightwave-electronic mixer at petahertz-scale frequencies is a first step toward making communication technology faster, and progresses research toward developing new, miniaturized lightwave electronic circuitry capable of handling optical signals directly at the nanoscale.

In the 1970s, scientists began exploring ways to extend electronic frequency mixing into the terahertz range using diodes. While these early efforts showed promise, progress stalled for decades. Recently, however, advances in nanotechnology have reignited this area of research. Researchers discovered that tiny structures like nanometer-length-scale needle tips and plasmonic antennas could function similarly to those early diodes but at much higher frequencies.

A recent open-access study published in Science Advances by Matthew Yeung, Lu-Ting Chou, Marco Turchetti, Felix Ritzkowsky, Karl K. Berggren, and Phillip D. Keathley at MIT has demonstrated a significant step forward. They developed an electronic frequency mixer for signal detection that operates beyond 0.350 PHz using tiny nanoantennae. These nanoantennae can mix different frequencies of light, enabling analysis of signals oscillating orders of magnitude faster than the fastest accessible to conventional electronics. Such petahertz electronic devices could enable developments that ultimately revolutionize fields that require precise analysis of extremely fast optical signals, such as spectroscopy and imaging, where capturing femtosecond-scale dynamics is crucial (a femtosecond is one-millionth of one-billionth of a second).

The team’s study highlights the use of nanoantenna networks to create a broadband, on-chip electronic optical frequency mixer. This innovative approach allows for the accurate readout of optical wave forms spanning more than one octave of bandwidth. Importantly, this process worked using a commercial turnkey laser that can be purchased off the shelf, rather than a highly customized laser.

While optical frequency mixing is possible using nonlinear materials, the process is purely optical (that is, it converts light input to light output at a new frequency). Furthermore, the materials have to be many wavelengths in thickness, limiting the device size to the micrometer scale (a micrometer is one-millionth of a meter).  In contrast, the lightwave-electronic method demonstrated by the authors uses a light-driven tunneling mechanism that offers high nonlinearities for frequency mixing and direct electronic output using nanometer-scale devices (a nanometer is one-billionth of a meter).

While this study focused on characterizing light pulses of different frequencies, the researchers envision that similar devices will enable one to construct circuits using light waves. This device, with bandwidths spanning multiple octaves, could provide new ways to investigate ultrafast light-matter interactions, accelerating advancements in ultrafast source technologies. 

This work not only pushes the boundaries of what is possible in optical signal processing but also bridges the gap between the fields of electronics and optics. By connecting these two important areas of research, this study paves the way for new technologies and applications in fields like spectroscopy, imaging, and communications, ultimately advancing our ability to explore and manipulate the ultrafast dynamics of light.

The research was initially supported by the U.S. Air Force Office of Scientific Research. Ongoing research into harmonic mixing is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences. Matthew Yeung acknowledges fellowship support from MathWorks, the U.S. National Science Foundation Graduate Research Fellowship Program, and MPS-Ascend Postdoctoral Research Fellowship. Lu-Ting Chou acknowledges financial support from the China's Ministry of Education for the Overseas Internship Program from the Chinese National Science and Technology Council for the doctoral fellowship program. This work was carried out, in part, through the use of MIT.nano.

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JSmol Viewer

Optimal signal wavelengths for underwater optical wireless communication under sunlight in stratified waters.

1. Introduction

• Investigated the effect of attenuation on the end-to-end diffused line-of-sight and point-to-point line-of-sight UOWC systems under eight realistic ocean stratification scenarios in order of increasing turbidity, under a “night condition”.
• Investigated the combined effect of signal attenuation and ambient solar radiation on end-to-end diffused line-of-sight and point-to-point line-of-sight UOWC systems under the eight stratification scenarios. This was evaluated for horizontal links and downlinks (transmitter is vertically above the receiver, with the receiver directly facing up in direct view of sunlight) by varying depth and distance.
• Shared novel insights on the wavelength preferences for the signal (400–700 nm) for the optimum optical signal-to-noise ratio (O-SNR) under the conditions described above, that seems to be connected to the transmitter and receiver parameters such as transmit power, beam divergence, and the receiver (photodetector) wavelength responsivity curve. The results have been shown with trends discussed for four selected profiles for brevity, in order of increasing turbidity.
• Demonstrated the variability of the maximum achievable link distance with depth based on the O-SNR (0 dB distance) correlated to the signal wavelength that achieves it.
• Shared insights on how these findings may be used to establish cooperative UOWC links for the optimal SNR, based on water profile, depth relative to the deep chlorophyll maximum, link orientation, and distance between the nodes. These findings may be useful for establishing UOWC within individual sites, such as aquaculture farms, or renewable energy sites if the site-specific water quality parameters are known and where the region of the UUV mission or connectivity of the UOWC network would span depths of the euphotic and disphotic zones.
• Provided re-evaluated coefficients to ensure the attenuation model detailed in Johnson et al. [ 33 ] is consistent with Uitz et al. [ 34 ].

2. Preliminaries

3. related works, 4. time-varying environmental influences, 4.1. downwelling ambient light, 4.2. stratified oceans, 5. system model, 5.1. haltrin’s single-parameter iop model based on concentration of chlorophyll-a, 5.2. ambient noise, 5.3. geometric propagation models common for uowc, 5.4. optical snr, 6. simulation and parameters, 6.1. transmitter and receiver parameters, 6.2. downlink and depth-variant horizontal link, 6.3. simulation, 7. results and discussion, 7.1. simulation results with no sunlight, 7.2. simulation results with sunlight, 8. conclusions, author contributions, data availability statement, conflicts of interest.

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Waduge, T.G.; Seet, B.-C.; Vopel, K. Optimal Signal Wavelengths for Underwater Optical Wireless Communication under Sunlight in Stratified Waters. J. Sens. Actuator Netw. 2024 , 13 , 54. https://doi.org/10.3390/jsan13050054

Waduge TG, Seet B-C, Vopel K. Optimal Signal Wavelengths for Underwater Optical Wireless Communication under Sunlight in Stratified Waters. Journal of Sensor and Actuator Networks . 2024; 13(5):54. https://doi.org/10.3390/jsan13050054

Waduge, Tharuka Govinda, Boon-Chong Seet, and Kay Vopel. 2024. "Optimal Signal Wavelengths for Underwater Optical Wireless Communication under Sunlight in Stratified Waters" Journal of Sensor and Actuator Networks 13, no. 5: 54. https://doi.org/10.3390/jsan13050054

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• Open access
• Published: 04 September 2024

Telecom-band multiwavelength vertical emitting quantum well nanowire laser arrays

• Xutao Zhang 1 ,
• Fanlu Zhang 2 ,
• Ruixuan Yi 3 ,
• Naiyin Wang 2 ,
• Zhicheng Su 2 ,
• Mingwen Zhang   ORCID: orcid.org/0000-0002-1989-4767 3 ,
• Bijun Zhao 3 ,
• Ziyuan Li 2 ,
• Jiangtao Qu   ORCID: orcid.org/0000-0003-0357-4205 4 ,
• Julie M. Cairney 4 ,
• Yuerui Lu   ORCID: orcid.org/0000-0001-6131-3906 5 ,
• Jianlin Zhao   ORCID: orcid.org/0000-0002-4619-1215 3 ,
• Xuetao Gan   ORCID: orcid.org/0000-0003-2469-5807 3 ,
• Hark Hoe Tan   ORCID: orcid.org/0000-0002-7816-537X 2 , 6 ,
• Chennupati Jagadish 2 , 6 &
• Lan Fu   ORCID: orcid.org/0000-0002-9070-8373 2 , 6  

Light: Science & Applications volume  13 , Article number:  230 ( 2024 ) Cite this article

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• Semiconductor lasers

Highly integrated optoelectronic and photonic systems underpin the development of next-generation advanced optical and quantum communication technologies, which require compact, multiwavelength laser sources at the telecom band. Here, we report on-substrate vertical emitting lasing from ordered InGaAs/InP multi-quantum well core–shell nanowire array epitaxially grown on InP substrate by selective area epitaxy. To reduce optical loss and tailor the cavity mode, a new nanowire facet engineering approach has been developed to achieve controlled quantum well nanowire dimensions with uniform morphology and high crystal quality. Owing to the strong quantum confinement effect of InGaAs quantum wells and the successful formation of a vertical Fabry–Pérot cavity between the top nanowire facet and bottom nanowire/SiO 2 mask interface, stimulated emissions of the EH 11a/b mode from single vertical nanowires from an on-substrate nanowire array have been demonstrated with a lasing threshold of ~28.2 μJ cm −2 per pulse and a high characteristic temperature of ~128 K. By fine-tuning the In composition of the quantum wells, room temperature, single-mode lasing is achieved in the vertical direction across a broad near-infrared spectral range, spanning from 940 nm to the telecommunication O and C bands. Our research indicates that through a carefully designed facet engineering strategy, highly ordered, uniform nanowire arrays with precise dimension control can be achieved to simultaneously deliver thousands of nanolasers with multiple wavelengths on the same substrate, paving a promising and scalable pathway towards future advanced optoelectronic and photonic systems.

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Introduction.

Semiconductor nanowires (NWs) offer compact, cost-effective, and low-threshold nanoscale lasers, ideal for applications in optical interconnects, medical diagnosis, and super-resolution imaging 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 . In particular, telecom-band NW lasers hold promise for on-chip coherent light sources in photonic integrated circuits, driving innovations in optical and quantum communication and computing 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 . To realize high-performance telecom-band NW lasers, it is imperative to focus on efficient gain media, optimal gain range, and effective optical cavity design 25 . This necessitates the epitaxial growth of high-quality NWs with smooth sidewalls, controlled dimensions, and precise crystal composition. Core–shell NWs with radial multiple-quantum wells (MQW) are attractive candidates due to their large active regions, tunable bandgap energy and quantum confinement effect, which are highly desirable for in nanoscale lasers.

However, epitaxial growth of MQW structures with both good structural and optical properties, along with uniform morphology, has proven to be a significant challenge. For instance, MQW NWs grown using the vapor-liquid-solid (VLS) method, such as GaAs/AlGaAs 26 and InGaAs/InP 27 , exhibit tapering and nonuniform morphologies. This leads to suboptimal optical confinement and a low-quality (Q) factor for the NW cavity. Selective area epitaxy (SAE) offers the potential to control QW NW morphology on various substrates 28 , 29 , 30 , 31 . However, achieving high crystallinity in GaAs-based NWs through the SAE technique remains problematic, resulting in defects like twinning and planar defects 32 . This leads to non-radiative recombination centers and degraded optical properties. Additionally, pure GaAs NWs suffer from high density of surface states, making room temperature lasing difficult without additional passivation steps 8 , 12 , 33 . In contrast, high-quality InP-based NWs with ultralow surface non-radiative recombination rates can be conveniently achieved using SAE technology, allowing for room temperature lasing 29 , 34 , 35 , 36 . But, many SAE-grown InGaAs/InP MQW NWs are based on wurtzite (WZ) crystal structure 37 , 38 , which exhibit complicated facet evolution and severe morphology deterioration, rendering them unsuitable for lasing due to their asymmetrical shape. By adjusting the growth window to low-temperature and high V/III ratio, highly uniform InGaAs/InP MQW NWs can be grown from InP NWs in a mixed zincblende (ZB) and WZ phase, which leads to the demonstration of room temperature optically pumped lasing at a wavelength of ~1 µm 39 . However, polytypic NWs also suffer from a high density of stacking faults and twin-plane defects, resulting in substantial internal optical loss. Moreover, the maximum achievable length of these polytypic InGaAs/InP MQW NWs is less than 2 µm due to limited axial growth under low-temperature and high V/III ratio conditions 39 . Such short NW lengths are more sensitive to temperature variations, which can lead to fluctuations in the lasing wavelength and reduced spectral purity of the laser output. In addition, the utmost importance lies in the direct growth of high-density NW lasers characterized by meticulously controlled sites and a flawlessly pristine surface, free from any processing damage. This precision in site control, coupled with an unblemished surface, enhances the practical significance of these lasers, rendering them indispensable for the seamless integration of photonic chips on a larger scale. While Chang et al. have explored diverse emission directions to achieve this objective 40 , 41 , the existing challenge lies in the lack of precise nanowire morphology control, preventing the attainment of high-quality optical cavities necessary for optical mode control. Thus, an effective growth strategy remains elusive for obtaining InGaAs/InP MQW NWs with high uniformity, crystal quality, and dimension controllability, which is crucial for efficient NW lasing.

In this work, we introduce an innovative multi-step facet engineering approach for WZ based InGaAs/InP MQW NW growth via SAE method. A \(\left\{1\bar{1}00\right\}\) faceted WZ InP NW core was firstly grown to the desired length under high-temperature and low V/III ratio conditions 42 followed by changing the growth conditions to low temperature and high V/III ratio to promote lateral InP shell growth with a 30° rotation of all NW sidewalls, transitioning from \(\left\{1\bar{1}00\right\}\) to { \(11\bar{2}0\) } orientation 43 , 44 , 45 . This allows for the subsequent InGaAs/InP MQW growth with a well-maintained hexagonal shape and smooth NW morphology, which is critical to facilitate the formation of a high- Q factor vertical Fabry–Pérot (F–P) cavity for MQW lasing in the vertical direction. Single mode vertical emitting laser centered at 1532 nm has been achieved at room temperature with a low threshold power of ∼ 28.2 μJ cm −2 per pulse and a high characteristic temperature of 128 K. By tuning the indium composition of the MQWs, tunable lasing peak has been achieved from 940 nm to telecommunication O and C band. Finally, simultaneous lasing is demonstrated from a substantial number of NWs in the same array, offering a promising path toward large-scale nano-laser integration.

To ensure sufficient optical gain and efficient radiative recombination with good optical confinement, a multi-step growth by selective area metal organic vapor phase epitaxy was developed. The NW array size is 200 ×200 μm with a pitch size of 800 nm and opening hole diameter of 120 nm. Firstly, core InP NWs are grown at a high temperature and low V/III ratio condition to obtain high crystal quality WZ NWs 42 , with a length of 4 μm. Then the growth conditions are switched to low temperature and high V/III ratio condition to enable uniform radial InGaAs/InP MQW growth 43 , 44 , 45 , which is schematically shown in Fig. 1a . Figure S1a, b present the schematic lateral/vertical cross-section and SEM images of WZ InP NW core, which is grown at 680 °C with a V/III ratio of (297), exhibiting a taper-free hexagonal cross-section with six \(\left\{1\bar{1}00\right\}\) sidewalls, as previously reported pure WZ-phase InP nanostructures 29 , 30 . Based on the \(\left\{1\bar{1}00\right\}\) faceted WZ InP NW core, an InP shell was grown at 610 °C with V/III ratio ~ 4200, with the schematic cross-section and SEM image of the core–shell structure shown in Fig. S1c, d , presenting a similar hexagonal shape with a {1 \(1\bar{2}\) 0} NW facet 30° rotated from the original \(\left\{1\bar{1}00\right\}\) NW facet and an enlarged diameter due to the enhanced lateral growth. Compared to WZ core-only NWs shown in Fig. S2 , the lifetime of InP core–shell NWs is greatly extended, indicating a significant improvement in optical properties, which could be attributed to the reduced surface recombination velocity in this core–shell-like structure 46 . More detailed comparison of WZ InP NW, mixed ZB/WZ InP NW and WZ InP core–shell NW can be found in Table S1 .

a Schematics of the WZ based InP NW, facet-rotated InP core–shell NW, and InGaAs/InP MQW NW, respectively. b 30° tilted SEM image of the NW array grown on InP(111)A substrate. c , d STEM-HAADF image of the lateral cross-section of an InGaAs/InP QW NW at different magnifications. The dashed blue line in (c) indicates the expected position of WZ InP NW core which is covered with the 30° rotated InP shell and MQW structure. e – g STEM-HAADF image taken along [112] zone axis from the vertical cross-section of NW top segment. The pink arrow in ( e ) indicates the NW growth along the (111)A direction. Line scan of EDX intensity along the radial QWs are superimposed in ( g ). h PL intensity map of the NW array with the intensity line scan from the region within the blue dashed lines showing uniform optical emission from the array

The excellent structural and optical properties of the WZ core–shell InP NWs form a good base for the subsequent InGaAs MQW incorporation (see Fig. S2 ). Following the growth of InP core–shell NWs, a 10-QW InGaAs/InP structure was sequentially grown with the detailed growth condition provided in Table S2 . The highly uniform morphology of the 10-QW NWs shown in Fig. S1e, f indicates a conformal MQW structure growth without any facet transition or morphology deterioration. The MQWs have the same sidewall orientation as the InP core–shell NW, confirming that {1 \(1\bar{2}\) 0} faceted NW can work as an ideal platform for MQW growth, in sharp contrast to \(\left\{1\bar{1}00\right\}\) faceted NW based MQW structure with asymmetrical morphology and complex facet evolution 38 , 47 . Compared with ZB/WZ mixed phase {110} faceted InGaAs/InP MQW NWs 37 , the WZ core–shell InP based MQW NWs offer more freedom in dimension engineering desirable for high-quality optical cavity design to modulate lasing peak and optical mode, as their length can be controlled by tuning the growth time of WZ InP NW core and diameter can be finely adjusted by changing the growth time of InP NW shell and MQW structures. Furthermore, defect-free or very few stacking faults can be achieved in WZ InP NWs, in comparison to the defective polytypic InP NW core, to enable higher crystal quality MQW growth for lasing. A detailed summary of various InGaAs/InP QW NWs achieved so far under different growth strategies is presented in Table S3 , showing the great advantages offered by this new facet engineering strategy for high-quality InGaAs/InP MQW NW growth.

The micro-structural analysis of the InGaAs/InP 10-QW NW was performed by scanning transmission electron microscopy (STEM) on lamellas prepared by focused ion beam (FIB) cross-sectioning. Figure 1c, d presents the high-angle annular dark-field (HAADF) images of the lateral cross-section of the NW at different magnifications. The InGaAs MQW layers can be distinguished as brighter rings due to the atomic mass difference induced Z-contrast, clearly showing the coaxial and symmetrical arrangement of alternating hexagonally shaped QWs and barriers. For this particular NW, the NW diameter is around 500 nm, and QW thickness is less than 2 nm. It is worth mentioning that the diameter of the NWs can be adjusted by pitch size, opening hole diameter, and the growth time. The QWs at the six corners of NW are \(\left\{1\bar{1}00\right\}\) faceted, and are relatively thicker compared to the QWs on {1 \(1\bar{2}\) 0} sidewalls, which is likely due to the larger atomic diffusion at the corners of the NWs 48 . While the \(\left\{1\bar{1}00\right\}\) faceted sidewalls have a very short length (< 5 nm), and gradually diminish with increased growth time, they do not affect overall NW morphology. Figure 1e and Fig. S3 show the vertical cross-sectional image of NW top segment along the NW growth direction. Both radial and axial QWs can be identified as brighter lines, with the alternating QWs and barriers continuously covering the {1 \(1\bar{2}\) 0} sidewalls. On the other hand, the thickness of the axial and radial QWs significantly reduces with the growth time, as a result of the gradual increase of NW diameter as well as a reduction in the axial growth rate at low temperature. High resolution HAADF images in Fig. 1f and Fig. S3b reveal a small ( \(\bar{1}102\) ) facet at the top corner of the WZ InP NW core, where the axial QWs merge with radial QWs and subsequently fill the conjunction region. The QW chemical composition analysis was performed by energy dispersive X-ray spectroscopy (EDX). Figure S5 – S7 shows the EDX maps of In, As, Ga and P elements at the top region from different NWs, clearly showing the presence of Ga and As in the QWs. For quantitative analysis of the chemical composition, EDX line-scans were performed across both the axial and radial QWs, and the results are superimposed with the HAADF image in Fig. 1g . Compared with the InP barrier layers, all the QW layers show a decreased In concentration commensurate with the increased Ga concentration and contain a P concentration due to interdiffusion effect. The average chemical composition for radial QWs is estimated to be In 0.85 Ga 0.15 As 0.4 P 0.6 .

Via this growth strategy, InGaAs/InP MQW NWs can be grown with highly uniform morphology, arranged in ordered arrays, with controlled length and diameter, as shown in Fig. 1b and Fig. S1f . The MQWs in different NWs within the same array appear to be relatively uniform, as seen in Fig. S4 . The optical properties of a single MQW NW were characterized by cathodoluminescence (CL) and PL spectroscopy at room temperature. Figure S8a presents the SEM and panchromatic CL image acquired by an InGaAs detector (wavelength coverage: 1–1.6 µm), showing strong emission from the MQW region due to the high quality of InGaAs QWs and InP barriers along the whole NW. Figure S8b presents the representative PL spectra measured on the top of NW array, showing the strong broadband emission covering 1.1–1.9 µm wavelength range, due to the contribution from both the axial and radial QWs, as well as the geometry/composition variance between the axial and radial InGaAs MQWs. The lifetime of QW emission is extracted to be ~0.25 ns, which is much lower compared with the lifetime of InP core–shell NW, indicating enhanced carrier recombination due to the quantum confinement effect (Fig. S8c ). In addition, the uniform and bright luminescence from ~100 NWs of the array is further verified by the PL intensity mapping as well as the line scan shown in Fig. 1h , indicating their great promise for large-scale device applications. It should be noted that, owing to the large lateral growth of the InGaAs MQWs and InP barriers, the NW diameter is much larger (>400 nm) than that of the SiO 2 opening (~120 nm), creating an effective refractive index contrast at the NW/SiO 2 interface for the formation of a vertical F-P cavity for each NW in the array which is attached to the substrate.

To evaluate the lasing properties, optical pumping of individual on-substrate NWs with a diameter of ~405 nm from another array was conducted in a home-made confocal micro-PL system at 5 K. Figure 2a shows the emission spectra from a single NW under different pump fluences. At low pump fluences, two peaks appear in the broad PL spectrum, which can be ascribed to emission from InP (865 nm) and InGaAs QWs (950 nm). When the pump fluence is increased to 2.5 μJ cm −2 per pulse, the QW peak becomes narrower and shifts to a shorter wavelength at 930 nm. As the pump fluence continues to increase, this narrow peak intensifies rapidly and dominates the entire emission spectrum while the spontaneous emission is clamped (see Fig. 2b ). The transition process from the spontaneous emission to the amplified spontaneous emission (ASE), and to the stimulated emission with increased pump fluence is verified by the typical “ S ”-shape of the L–L curve (light output versus light input curve) shown in Fig. 2c . The corresponding full width at half maximum (FWHM) as a function of pump fluence is also displayed as blue dots in Fig. 2c . It can be found that the gain is not sufficient to dominate over the entire spectrum, and spontaneous emission dominates at low pump fluences, resulting in a broad emission spectrum; As the pump fluence increases, more carriers are excited to higher energy states, achieving population inversion. As the pump power further increases, the population inversion grows and the gain of the medium increases. The gain of the lasing mode is typically highest at the peak of the gain spectrum. As the gain increases, the feedback mechanism of the laser preferentially amplifies the wavelengths near the peak of the gain spectrum more than those at the wings, resulting in spectral narrowing. Then, a dramatic drop in FWHM can be clearly seen at threshold, indicating that single mode lasing with a low lasing threshold P th of 2.7 μJ cm −2 per pulse is achieved in this vertically standing NW.

a Emission spectrum at different pump fluences. b Normalized emission intensity spectral map as a function of pump fluence. c Lasing emission intensity (red) and the corresponding FWHM of the spectrum (blue) as a function of pump fluence plotted on a logarithmic-logarithmic scale. d Normalized time-resolved emission decays from the InP, InGaAs QWs and lasing peak at different pump fluences

To evaluate the luminescent efficiency of these MQW NWs, time-resolved PL decay measurements are carried out for the various peak positions of the spectra under different excitation fluences, as shown in Fig. 2d . Figure S9 shows the different peaks originate from the InP centered at 865 nm (P1), spontaneous emission from InGaAs QWs centered at 955 nm below lasing threshold (P2), and lasing peak centered at 930 nm above threshold (P3), respectively. Figure 2d shows two mono-exponential decays and one double-exponential decays corresponding to P1, P2 and P3, respectively. Accordingly, the spontaneous emission lifetimes can be estimated as 715 ps for the InP and 444 ps for InGaAs QWs by fitting the time decay plots of P1 and P2 with a mono-exponential curve 49 . The difference in the carrier lifetime indicates that the radiative recombination rate of carriers in the QWs is faster than that in the InP due to quantum confinement effect 49 , 50 . Above the lasing threshold, the time decay of the P3 comprise a resolution-limited stimulated emission lifetime ~ 35 ps in the early stages and a longer spontaneous emission lifetime ~168 ps of the at the later stage, indicating that the former dominates the entire PL spectrum. All the results show that these core–shell MQW NWs have achieved excellent carrier confinement effect, enabling high radiative recombination efficiency and lasing.

To understand the lasing mode of these InGaAs/InP MQW NWs, a threshold gain analysis for different NW diameters was carried out using finite difference time domain (FDTD) simulation. Around the lasing peak (930 nm), the threshold gains of the possible lasing modes (HE 11a , HE 11b , TE 01 , TM 01 , HE 21a , HE 21b , EH 11a , EH 11b ) are calculated according to 14 , 33 , 51 , 52

where Γ is the confinement factor, L is the length of the NW, R 1 and R 2 are the reflectivity of the top and bottom facets of the standing NW, respectively. In this vertically standing F-P cavity, the top mirror is formed by the InP/air interface and the bottom mirror is formed by the InP/SiO 2 interface. The reflectivity of the top facet of the vertical NW is plotted in Fig. 3a for different modes. For the bottom mirror, the NW core with a diameter of 120 nm is in direct contact with the substrate. Because the majority of the energy of the HE 11a , HE 11b , and TM 01 modes is concentrated at the central axis of the NW, they leak into InP substrate. Therefore, the reflectivity of these modes is reduced significantly. The TE 01 , HE 21a , HE 21b , and EH 11a and EH 11b modes on the other hand, have the majority of their energy distributed at the periphery of the NW, resulting in relatively high reflectivity because of the interface with SiO 2 , as shown in Fig. 3b . The bottom surface reflectivity can be further increased by depositing a thicker SiO 2 layer to lower the lasing threshold (see Fig. S10 ). After calculating the optical confinement factors Γ (see Fig. 3c ), the curves of threshold gain versus NW diameter can be obtained, as plotted in Fig. 3d . For the NW with a diameter of 405 nm as studied experimentally in this work, the lowest threshold modes are the two degenerate transverse EH 11a and EH 11b modes. To confirm the simulation result, polarization dependence of the emission spectrum is further conducted. From the lasing intensity versus polarization angle plot shown in Fig. 3e, f , it can be verified that this lasing mode is linearly polarized with an extinction ratio \(\rho =\left({I}_{\parallel }-{I}_{\perp }\right)/\left({I}_{\parallel }+{I}_{\perp }\right)\) of 45%. By comparing the electric field distributions of the possible supported modes shown in Fig. S11 , the polarization dependence is in good agreement with the EH 11a/b mode.

Top ( a ) and bottom ( b ) surface reflectivity, confinement factor ( c ) and the calculated threshold gain ( d ) versus NW diameter for each transverse mode. e , f Polarization dependence emission spectra ( e ) and the lasing intensity polar plot ( f ) measured from a MQW NW

High-performance telecom-band nanoscale lasers are desirable for silicon-based on-chip optoelectronic integrated circuits due to the low transmission loss. To further extend the lasing wavelength to meet this important application requirement, InGaAs/InP MQW NW arrays were grown with different indium compositions in the QWs. Figure 4a shows the emission spectra from a single NW of the array samples. With an increasing pump fluence, the InGaAs QW peak can be seen evolving from a broad PL emission to single mode lasing centered at 1532 nm at room temperature with a low threshold power of ∼ 28.2 μJ cm −2 per pulse, as observed in the spectral intensity map in the inset of Fig. 4a . The typical “ S ”-shape in L-L curve and the variation of FWHM with increasing pump power illustrates the stimulated emission process of the NWs, as shown in Fig. 4b . The plot of lasing threshold as a function of operating temperature is shown in Fig. 4c , where the lasing peak is slightly redshifted from 1516.9 to 1532.4 nm with temperature due to the changes of the bandgap and refractive index (see the inset of Fig. 4c and Fig. S12 ). Figure S12 shows the lasing spectra and the corresponding L-L curves under different operating temperatures. By fitting the data in Fig. 4c with \({p}_{{\rm{th}}}={e}^{t/{t}_{0}}\) , the characteristic temperature is estimated to be 128 K, which is comparable with those of the reported for horizontal NW lasers 33 , 49 , 51 , indicating good temperature characteristics despite without any heatsinking. Lasing peak tunability in the telecom-band is another important requirement for optical communication systems. By changing In composition in the InGaAs QWs lasing in the telecommunication bands, including O-band, E-band, S-band and C-band, can be achieved as shown in Fig. 4d from 1356 to 1542 nm. The corresponding power-dependent lasing spectra can be found in Fig. S14 . To the best of our knowledge, this is the first report on lasing from a single NW vertically standing in a site-controlled NW arrays at room temperature, with tunable wavelengths covering the whole telecom band.

a Emission spectra at different pump fluences of a single standing NW. Inset shows the corresponding normalized spectral intensity map. b Emission intensity (red) and the corresponding FWHM (blue) of the spectra as a function of pump fluence. c Lasing threshold versus operation temperature. The dashed line is a fitting of the experimental data to extract the characteristic temperature. Inset shows the lasing spectrum at various temperatures. d Telecom-band lasing spectra from NWs with different indium compositions in the QWs at room temperature under a pump fluence of 1.3 P th . e , f Optical images from the NW array before ( e ), and after ( f ) lasing threshold. g Image on ( f ) under an attenuated pump fluence

To confirm the uniform lasing properties of the NW array, which is a crucial prerequisite to construct the large-scale active nanoscale lasers with stable, reliable and uniform performance for optoelectronic/photonic integrated circuits, PL intensity imaging is further conducted. Figure 4e shows 4 faint luminescence spots from the NW array under low pump fluence. Limited by our optical setup, only 4 NWs can be observed in one image at the same time. Above lasing threshold, these 4 NWs show simultaneous lasing with the emitted coherent light forming speckle fringes, as displayed in Fig. 4f . The corresponding emission spectra, normalized spectral intensity map and L-L curves are also shown in Fig. S16 . By placing an attenuator in the collection path to prevent overexposure, 4 bright spots can be observed from the NW top facets shown in Fig. 4g , further validating the good uniformity of NW lasers. To demonstrate the generality of these standing NW lasing, PL images of multiple NWs from different locations within the same array can be found in Fig. S15 . In addition, video 1 shows the variations in PL emission from multiple NWs as the pump fluence increased. The video demonstrates the process of PL emission changing from spontaneous emission to stimulated emission as the pump intensity increases, showing that the lasing thresholds of different NWs vary slightly. In conclusion, the collective lasing phenomenon of these NW arrays shows great potential as a promising candidate to produce large scale high density nanolaser sources.

InGaAs/InP core–shell MQW NW arrays have been grown using selective area epitaxy following a carefully designed multi-step growth strategy to control NW length and diameter to achieve uniform morphology, strong carrier confinement, sufficient optical gain, and vertical F–P cavities. Vertical lasing from individual NWs of the vertical arrays is achieved. By adjusting the In composition of the MQWs, the lasing peak can be tuned over a wide range across the telecommunication O-band to C-band window at room temperature. The vertical emission direction, low threshold, high characteristic temperature, as well as uniform lasing simultaneously from a large number of individual NWs within the NW array provide a promising scalable pathway towards cost-effective on-chip advanced optoelectronic and photonic integrated circuits.

Materials and methods

Optical experiment method.

A confocal photoluminescence microscopy system was used for the optical characterization of the NWs. A frequency-doubled pulsed laser (19.8 MHz, pulse width 11 ps, 532 nm) was used to excite the NWs. The PL emission from the NW was detected by an InGaAs CCD. A linear polarizer was inserted in the signal collection path of the optical system to perform polarization analysis for NW lasing. The carrier lifetime measurement was performed using a time-correlated single photon counting (TCSPC) system composed of an attached single photon detector (SPD, resolution: 50 ps) and a Multi-Channel Scaling (MCS) board (resolution: 25 ps). For low-temperature measurements, a cryostat operating in the range of 4 to 300 K was used.

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Acknowledgements

This work is supported by the Key Research and Development Program (2022YFA1404800), the National Natural Science Foundation of China (62375226, 62375225, 12374359, 62105267), the Fundamental Research Funds for the Central Universities (23GH02023) and the Analytical & Testing Center of Northwestern Polytechnical University and the Australian Research Council.The Australian National Fabrication Facility ACT Node is acknowledged for access to the epitaxial growth facilities.

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Frontiers Science Center for Flexible Electronics, Xi’an Institute of Flexible Electronics (IFE) and Xi’an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, 127 West Youyi Road, 710072, Xi’an, China

Xutao Zhang

Department of Electronic Materials Engineering, Research School of Physics, The Australian National University, Canberra, ACT 2600, Australia

Fanlu Zhang, Naiyin Wang, Zhicheng Su, Ziyuan Li, Hark Hoe Tan, Chennupati Jagadish & Lan Fu

Key Laboratory of Light Field Manipulation and Information Acquisition, Ministry of Industry and Information Technology, and Shaanxi Key Laboratory of Optical Information Technology, School of Physical Science and Technology, Northwestern Polytechnical University, 710129, Xi’an, China

Ruixuan Yi, Mingwen Zhang, Bijun Zhao, Jianlin Zhao & Xuetao Gan

Australian Centre for Microscopy and Microanalysis, the University of Sydney, Sydney, NSW 2006, Australia

Jiangtao Qu & Julie M. Cairney

School of Engineering, College of Engineering, The Australian National University, Canberra, ACT 2600, Australia

ARC Centre of Excellence for Transformative Meta-Optical Systems, Research School of Physics, The Australian National University, Canberra, ACT 2600, Australia

Hark Hoe Tan, Chennupati Jagadish & Lan Fu

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Contributions

X.Z., X.G., and L.F. conceived and designed the experiments. X.Z., F.Z., N.W., and Z.L carried out the NW growth, B.Z., J.Q., and J.C. carried out the TEM analysis. X.Z., F.Z., R.Y., Z.S., and M.Z. performed the optical lasing characterization. X.Z. and R.Y. carried out the simulations and analyses. J.Z., Y.L., and L.F. contributed to the discussion of experimental results. X.Z., F.Z., X.G., and L.F. wrote the manuscript with contributions from all other authors. X.G., H.H., C.J., and L.F. supervised the whole project. X.Z. and F.Z. contributed equally to this work.

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Correspondence to Xuetao Gan or Lan Fu .

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Supplementary information for telecom-band multiwavelength vertical emitting quantum well nanowire laser arrays, rights and permissions.

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Zhang, X., Zhang, F., Yi, R. et al. Telecom-band multiwavelength vertical emitting quantum well nanowire laser arrays. Light Sci Appl 13 , 230 (2024). https://doi.org/10.1038/s41377-024-01570-7

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Received : 27 February 2024

Revised : 26 July 2024

Accepted : 08 August 2024

Published : 04 September 2024

DOI : https://doi.org/10.1038/s41377-024-01570-7

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3 Communication Stocks to Gain From Integrated Cloud & Fiber Network

The Zacks Communication - Components industry appears well poised to benefit from healthy demand trends and an increasing propensity for users to stay abreast of the latest digital innovations. However, price volatility due to elevated customer inventory levels, high capital expenditures for infrastructure upgrades, margin erosion, geopolitical conflicts and raging wars have dented the industry’s profitability. Nevertheless, Arista Networks, Inc. ( ANET Quick Quote ANET - Free Report ) , Harmonic Inc. ( HLIT Quick Quote HLIT - Free Report ) and AudioCodes Ltd. ( AUDC Quick Quote AUDC - Free Report ) are likely to gain in the long run as demand for scalable infrastructure for seamless connectivity rises with the wide proliferation of IoT, fiber densification, transition to cloud and accelerated 5G rollout.

Industry Description

The Zacks Communication - Components industry primarily comprises companies that provide diverse telecom products and services to develop scalable network architecture, demand-driven video solutions and broadband access equipment. These include various building blocks such as small cells, routers and antennas incorporated into equipment and facilities, and subsequently utilized by service providers to build networks for end users. Their product portfolio encompasses optical and copper connectivity products, hybrid fiber-coaxial equipment, edge routers, metro Wi-Fi, storage and distribution equipment for cable TV operators, modems, EMTAs (Embedded Multimedia Terminal Adapter), gateways, set-top boxes, analog and digital microphones, audio processors, glass substrates for LCD TVs and notebooks, ceramic substrates for mobile and laboratory filtration products.

What's Shaping the Future of the Communication Components Industry?

Network Convergence: With operators moving toward converged or multi-use network structures, combining voice, video and data communications into a single network, the industry is increasingly developing solutions with steady R&D investments to support wireline and wireless network convergence. These investments are likely to help minimize service delivery costs to adequately support broadband competition and expand rural coverage and wireless densification. The industry players have enabled enterprises to rapidly scale communications functionalities to a vast range of applications and devices with easy-to-use software application programming interfaces. The firms support high user volumes without affecting deliverability and cost-effectively eliminate performance degradation. Focus on Cloud & Fiber Densification: The firms are likely to benefit from a software-driven, data-centric approach that helps customers build their cloud architecture and enhance the cloud experience. The industry participants are well-poised for growth in data-driven cloud networking business with proactive platforms and predictive operations. Fiber networks are essential for the growing deployment of small cells that bring the network closer to the user and supplement macro networks to provide extensive coverage. Telecom service providers are increasingly leaning toward fiber optic cable to meet the burgeoning demand for cloud-based business data and video streaming services by individuals. Moreover, the fiber-optic cable network is vital for backhaul and last-mile local loops, which are required by wireless service providers to deploy the 5G network. Robust Demand for Quality Products: As both consumers and enterprises are using networks more extensively, there is tremendous demand for quality networking components. Additionally, data consumption patterns are changing, with a growing propensity to consume more video content, creating the need for faster data transfer. Since optical networks are more efficient and most existing networks are copper-based, the demand for optical solutions is strong. The industry firms offer several products focused on the data center, with a typical portfolio consisting of optical fiber, hardware, cables and connectors, enabling them to meet the evolving customer requirements and bridge the digital divide across the United States. Waning Profits: Although supply chain woes have declined progressively, the industry is facing a dearth of chips, which are the building blocks of various equipment used by telecom carriers. Moreover, high raw material prices due to the Middle East tensions, the prolonged Russia-Ukraine war and the consequent economic sanctions against the Putin regime have affected the operation schedule of various firms. High technological obsolescence of most products has escalated operating costs, while high customer inventory levels and a conservative approach toward placing orders for high-value items remain headwinds.

Zacks Industry Rank Indicates Bullish Trends

The Zacks Communication - Components industry is housed within the broader Zacks Computer and Technology sector. It carries a Zacks Industry Rank #21, which places it among the top 8% of more than 250 Zacks industries. The group’s Zacks Industry Rank , which is basically the average of the Zacks Rank of all the member stocks, indicates solid prospects. Our research shows that the top 50% of the Zacks-ranked industries outperform the bottom 50% by a factor of more than 2 to 1.  Before we present a few communication component stocks that are well-positioned to outperform the market based on a strong earnings outlook, let’s take a look at the industry’s recent stock market performance and valuation picture.

Industry Outperforms S&P 500, Sector

The Zacks Communication - Infrastructure industry has outperformed the S&P 500 composite and the broader Zacks Computer and Technology sector over the past year. The industry has rallied 51.8% over this period compared with the S&P 500 and sector’s rise of 23.6% and 27%, respectively. One-Year Price Performance

Industry's Current Valuation

On the basis of the trailing 12-month price-to-book (P/B), the industry is currently trading at 5.95X compared with the S&P 500’s 8.4X. It is also below the sector’s trailing 12-month P/B of 9.35X. Over the past five years, the industry has traded as high as 6.58X, as low as 2.03X and at the median of 3.58X, as the chart below shows. Trailing 12-Month price-to-book (P/B) Ratio

3 Communication Components Stocks to Bet on

Arista: Santa Clara, CA-based Arista provides cloud networking solutions for data centers and cloud computing environments. It offers one of the broadest product lines of data center and Ethernet switches and routers in the industry. The stock has surged 67.9% over the past year. The Zacks Consensus Estimate for the current and next fiscal earnings has been revised 23.4% and 22.4% upward, respectively, over the past year. It has a long-term earnings growth expectation of 17.2% and delivered an earnings surprise of 15%, on average, in the trailing four quarters. Arista continues to benefit from the expanding cloud networking market, driven by strong demand for scalable infrastructure. In addition to high capacity and easy availability, its cloud networking solutions promise predictable performance and programmability that enable integration with third-party applications for network management, automation and orchestration. Arista currently carries a Zacks Rank #2 (Buy). You can see the complete list of today’s Zacks #1 Rank (Strong Buy) stocks here . Price and Consensus: ANET

Price and Consensus: HLIT

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