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Shekhar S, Bogaerts W, Chrostowski L, Bowers JE, Hochberg M, Soref R, Shastri BJ. Roadmapping the next generation of silicon photonics. Nat Commun 2024; 15:751. [PMID: 38272873 PMCID: PMC10811194 DOI: 10.1038/s41467-024-44750-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 01/03/2024] [Indexed: 01/27/2024] Open
Abstract
Silicon photonics has developed into a mainstream technology driven by advances in optical communications. The current generation has led to a proliferation of integrated photonic devices from thousands to millions-mainly in the form of communication transceivers for data centers. Products in many exciting applications, such as sensing and computing, are around the corner. What will it take to increase the proliferation of silicon photonics from millions to billions of units shipped? What will the next generation of silicon photonics look like? What are the common threads in the integration and fabrication bottlenecks that silicon photonic applications face, and which emerging technologies can solve them? This perspective article is an attempt to answer such questions. We chart the generational trends in silicon photonics technology, drawing parallels from the generational definitions of CMOS technology. We identify the crucial challenges that must be solved to make giant strides in CMOS-foundry-compatible devices, circuits, integration, and packaging. We identify challenges critical to the next generation of systems and applications-in communication, signal processing, and sensing. By identifying and summarizing such challenges and opportunities, we aim to stimulate further research on devices, circuits, and systems for the silicon photonics ecosystem.
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Affiliation(s)
- Sudip Shekhar
- Department of Electrical & Computer Engineering, University of British Columbia, 2332 Main Mall, Vancouver, V6T1Z4, BC, Canada.
| | - Wim Bogaerts
- Department of Information Technology, Ghent University - IMEC, Technologiepark-Zwijnaarde 126, Ghent, 9052, Belgium
| | - Lukas Chrostowski
- Department of Electrical & Computer Engineering, University of British Columbia, 2332 Main Mall, Vancouver, V6T1Z4, BC, Canada
| | - John E Bowers
- Department of Electrical & Computer Engineering, University of California Santa Barbara, Santa Barbara, 93106, CA, USA
| | - Michael Hochberg
- Luminous Computing, 4750 Patrick Henry Drive, Santa Clara, 95054, CA, USA
| | - Richard Soref
- College of Science and Mathematics, University of Massachusetts Boston, 100 William T. Morrissey Blvd., Boston, 02125, MA, USA
| | - Bhavin J Shastri
- Department of Physics, Engineering Physics & Astronomy, Queen's University, 64 Bader Lane, Kingston, K7L3N6, ON, Canada.
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2
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Zhang W, Lederman JC, Ferreira de Lima T, Zhang J, Bilodeau S, Hudson L, Tait A, Shastri BJ, Prucnal PR. A system-on-chip microwave photonic processor solves dynamic RF interference in real time with picosecond latency. Light Sci Appl 2024; 13:14. [PMID: 38195653 PMCID: PMC10776583 DOI: 10.1038/s41377-023-01362-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 01/11/2024]
Abstract
Radio-frequency interference is a growing concern as wireless technology advances, with potentially life-threatening consequences like interference between radar altimeters and 5 G cellular networks. Mobile transceivers mix signals with varying ratios over time, posing challenges for conventional digital signal processing (DSP) due to its high latency. These challenges will worsen as future wireless technologies adopt higher carrier frequencies and data rates. However, conventional DSPs, already on the brink of their clock frequency limit, are expected to offer only marginal speed advancements. This paper introduces a photonic processor to address dynamic interference through blind source separation (BSS). Our system-on-chip processor employs a fully integrated photonic signal pathway in the analogue domain, enabling rapid demixing of received mixtures and recovering the signal-of-interest in under 15 picoseconds. This reduction in latency surpasses electronic counterparts by more than three orders of magnitude. To complement the photonic processor, electronic peripherals based on field-programmable gate array (FPGA) assess the effectiveness of demixing and continuously update demixing weights at a rate of up to 305 Hz. This compact setup features precise dithering weight control, impedance-controlled circuit board and optical fibre packaging, suitable for handheld and mobile scenarios. We experimentally demonstrate the processor's ability to suppress transmission errors and maintain signal-to-noise ratios in two scenarios, radar altimeters and mobile communications. This work pioneers the real-time adaptability of integrated silicon photonics, enabling online learning and weight adjustments, and showcasing practical operational applications for photonic processing.
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Affiliation(s)
- Weipeng Zhang
- Department of Electrical and Computer Engineering, Princeton University, Princeton, 08544, NJ, USA.
| | - Joshua C Lederman
- Department of Electrical and Computer Engineering, Princeton University, Princeton, 08544, NJ, USA
| | | | - Jiawei Zhang
- Department of Electrical and Computer Engineering, Princeton University, Princeton, 08544, NJ, USA
| | - Simon Bilodeau
- Department of Electrical and Computer Engineering, Princeton University, Princeton, 08544, NJ, USA
| | - Leila Hudson
- Department of Electrical and Computer Engineering, Princeton University, Princeton, 08544, NJ, USA
| | - Alexander Tait
- Department of Electrical and Computer Engineering, Queen's University, Kingston, K7L 3N6, Ontario, Canada
| | - Bhavin J Shastri
- Department of Physics, Engineering Physics and Astronomy, Queen's University, Kingston, K7L 3N6, Ontario, Canada
| | - Paul R Prucnal
- Department of Electrical and Computer Engineering, Princeton University, Princeton, 08544, NJ, USA.
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3
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Lederman JC, Zhang W, de Lima TF, Blow EC, Bilodeau S, Shastri BJ, Prucnal PR. Real-time photonic blind interference cancellation. Nat Commun 2023; 14:8197. [PMID: 38081807 PMCID: PMC10713617 DOI: 10.1038/s41467-023-43982-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 11/24/2023] [Indexed: 02/28/2024] Open
Abstract
mmWave devices can broadcast multiple spatially-separated data streams simultaneously in order to increase data transfer rates. Data transfer can, however, be compromised by interference. Photonic blind interference cancellation systems offer a power-efficient means of mitigating interference, but previous demonstrations of such systems have been limited by high latencies and the need for regular calibration. Here, we demonstrate real-time photonic blind interference cancellation using an FPGA-photonic system executing a zero-calibration control algorithm. Our system offers a greater than 200-fold reduction in latency compared to previous work, enabling sub-second cancellation weight identification. We further investigate key trade-offs between system latency, power consumption, and success rate, and we validate sub-Nyquist sampling for blind interference cancellation. We estimate that photonic interference cancellation can reduce the power required for digitization and signal recovery by greater than 74 times compared to the digital electronic alternative.
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Affiliation(s)
- Joshua C Lederman
- Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, 08544, USA.
| | - Weipeng Zhang
- Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, 08544, USA
| | - Thomas Ferreira de Lima
- Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, 08544, USA
- NEC Laboratories America, Princeton, NJ, 08540, USA
| | - Eric C Blow
- Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, 08544, USA
- NEC Laboratories America, Princeton, NJ, 08540, USA
| | - Simon Bilodeau
- Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, 08544, USA
| | - Bhavin J Shastri
- Department of Physics, Engineering Physics & Astronomy, Queen's University, Kingston, ON, K7L 3N6, Canada
| | - Paul R Prucnal
- Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, 08544, USA
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4
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Marquez BA, Singh J, Morison H, Guo Z, Chrostowski L, Prucnal PR, Shekhar S, Shastri BJ. Fully-integrated photonic tensor core for image convolutions. Nanotechnology 2023. [PMID: 37321201 DOI: 10.1088/1361-6528/acde83] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Convolutions are one of the most important operations in both signal and image processing. From spectral analysis to computer vision, convolutional filtering is often related to spatial information processing where neighborhood operations are involved. As convolution operations are based around the product of two functions, vectors or matrices, dot products play a key role in the performance of such operations; for example, advanced image processing techniques require fast, dense matrix multiplications that typically take more than 90% of the computational capacity dedicated to solving convolutional neural networks. Silicon photonics has been demonstrated to be an ideal candidate to accelerate information processing involving parallel matrix multiplications. In this work, we experimentally demonstrate a multiwavelength approach with fully integrated modulators, tunable filters as microring resonator weight banks, and a balanced detector to perform matrix multiplications for image convolution operations. We develop a scattering matrix model that matches the experiment to simulate large-scale versions of these photonic systems with which we predict performance and physical constraints, including inter-channel cross-talk and bit resolution.
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Affiliation(s)
- Bicky A Marquez
- Physics, Queen's University, 64 Bader Ln, Kingston, Ontario, K7L 3N6, CANADA
| | - Jagmeet Singh
- Physics, Queen's University, 64 Bader Ln, Kingston, Ontario, K7L 3N6, CANADA
| | - Hugh Morison
- Physics, Queen's University, 64 Bader Lane, Kingston, Ontario, K7L3N6, CANADA
| | - Zhimu Guo
- Queen's University, 64 bader ln, Kingston, Ontario, K7L 3N6, CANADA
| | - Lukas Chrostowski
- Department of Physics, University of British Columbia, 3333 University Way, Kelawna, British Columbia, Vancouver, British Columbia, V1V 1Y7, CANADA
| | - Paul R Prucnal
- Department of Electrical Engineering, Princeton University, Engineering Quadrangle, Olden Street, Princeton, NJ 08544, USA, Princeton, NJ 08544, UNITED STATES
| | - Sudip Shekhar
- The University of British Columbia, Department of Electrical and Computer Engineering 5500-2332 Main Mall, Vancouver, British Columbia, V1V 1Y7, CANADA
| | - Bhavin J Shastri
- Physics, Engineering Physics & Astronomy , Queen's University, 64 Bader Lane, Stirling Hall, Kingston, Ontario, K7L 3N6, CANADA
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5
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Buckley SM, Tait AN, McCaughan AN, Shastri BJ. Photonic online learning: a perspective. Nanophotonics 2023; 12:833-845. [PMID: 36909290 PMCID: PMC9995662 DOI: 10.1515/nanoph-2022-0553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/31/2022] [Accepted: 12/03/2022] [Indexed: 06/18/2023]
Abstract
Emerging neuromorphic hardware promises to solve certain problems faster and with higher energy efficiency than traditional computing by using physical processes that take place at the device level as the computational primitives in neural networks. While initial results in photonic neuromorphic hardware are very promising, such hardware requires programming or "training" that is often power-hungry and time-consuming. In this article, we examine the online learning paradigm, where the machinery for training is built deeply into the hardware itself. We argue that some form of online learning will be necessary if photonic neuromorphic hardware is to achieve its true potential.
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Affiliation(s)
- Sonia Mary Buckley
- Applied Physics Division, National Institute of Standards and Technology, Boulder, CO80305, USA
| | - Alexander N. Tait
- Department of Physics, Engineering Physics and Astronomy, Queen’s University, Kingston, ON, Canada
| | - Adam N. McCaughan
- Applied Physics Division, National Institute of Standards and Technology, Boulder, CO80305, USA
| | - Bhavin J. Shastri
- Department of Physics, Engineering Physics and Astronomy, Queen’s University, Kingston, ON, Canada
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6
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Tamura M, Morison H, Shastri BJ. Inducing optical self-pulsation by electrically tuning graphene on a silicon microring. Nanophotonics 2022; 11:4017-4025. [PMID: 36081448 PMCID: PMC9394513 DOI: 10.1515/nanoph-2022-0077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 04/19/2022] [Indexed: 06/15/2023]
Abstract
A mechanism for self-pulsation in a proposed graphene-on-silicon microring device is studied. The relevant nonlinear effects of two photon absorption, Kerr effect, saturable absorption, free carrier absorption, and dispersion are included in a coupled mode theory framework. We look at the electrical tunability of absorption and the Kerr effect in graphene. We show that the microring can switch from a stable rest state to a self-pulsation state by electrically tuning the graphene under constant illumination. This switching is indicative of a supercritical Hopf bifurcation since the frequency of the pulses is approximately constant at 7 GHz and the amplitudes initial grow with increasing Fermi level. The CMOS compatibility of graphene and the opto-electronic mechanism allows this to device to be fairly easily integrated with other silicon photonic devices.
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Affiliation(s)
- Marcus Tamura
- Department of Physics, Engineering Physics and Astronomy, Queen’s University, Kingston, Canada
| | - Hugh Morison
- Department of Physics, Engineering Physics and Astronomy, Queen’s University, Kingston, Canada
| | - Bhavin J. Shastri
- Department of Physics, Engineering Physics and Astronomy, Queen’s University, Kingston, Canada
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7
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Berggren K, Xia Q, Likharev KK, Strukov DB, Jiang H, Mikolajick T, Querlioz D, Salinga M, Erickson JR, Pi S, Xiong F, Lin P, Li C, Chen Y, Xiong S, Hoskins BD, Daniels MW, Madhavan A, Liddle JA, McClelland JJ, Yang Y, Rupp J, Nonnenmann SS, Cheng KT, Gong N, Lastras-Montaño MA, Talin AA, Salleo A, Shastri BJ, de Lima TF, Prucnal P, Tait AN, Shen Y, Meng H, Roques-Carmes C, Cheng Z, Bhaskaran H, Jariwala D, Wang H, Shainline JM, Segall K, Yang JJ, Roy K, Datta S, Raychowdhury A. Roadmap on emerging hardware and technology for machine learning. Nanotechnology 2021; 32:012002. [PMID: 32679577 DOI: 10.1088/1361-6528/aba70f] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.
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Affiliation(s)
- Karl Berggren
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
| | - Qiangfei Xia
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, United States of America
| | | | - Dmitri B Strukov
- Department of Electrical and Computer Engineering, University of California at Santa Barbara, Santa Barbara, CA 93106, United States of America
| | - Hao Jiang
- School of Engineering & Applied Science Yale University, CT, United States of America
| | | | | | - Martin Salinga
- Institut für Materialphysik, Westfälische Wilhelms-Universität Münster, Germany
| | - John R Erickson
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15261, United States of America
| | - Shuang Pi
- Lam Research, Fremont, CA, United States of America
| | - Feng Xiong
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15261, United States of America
| | - Peng Lin
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
| | - Can Li
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Yu Chen
- School of information science and technology, Fudan University, Shanghai, People's Republic of China
| | - Shisheng Xiong
- School of information science and technology, Fudan University, Shanghai, People's Republic of China
| | - Brian D Hoskins
- Physical Measurements Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, United States of America
| | - Matthew W Daniels
- Physical Measurements Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, United States of America
| | - Advait Madhavan
- Physical Measurements Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, United States of America
- Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, MD, United States of America
| | - James A Liddle
- Physical Measurements Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, United States of America
| | - Jabez J McClelland
- Physical Measurements Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, United States of America
| | - Yuchao Yang
- School of Electronics Engineering and Computer Science, Peking University, Beijing, People's Republic of China
| | - Jennifer Rupp
- Department of Materials Science and Engineering and Department of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
- Electrochemical Materials, ETHZ Department of Materials, Hönggerbergring 64, Zürich 8093, Switzerland
| | - Stephen S Nonnenmann
- Department of Mechanical & Industrial Engineering, University of Massachusetts-Amherst, MA, United States of America
| | - Kwang-Ting Cheng
- School of Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, People's Republic of China
| | - Nanbo Gong
- IBM T J Watson Research Center, Yorktown Heights, NY 10598, United States of America
| | - Miguel Angel Lastras-Montaño
- Instituto de Investigación en Comunicación Óptica, Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, México
| | - A Alec Talin
- Sandia National Laboratories, Livermore, CA 94551, United States of America
| | - Alberto Salleo
- Department of Materials Science and Engineering, Stanford University, Stanford, California, United States of America
| | - Bhavin J Shastri
- Department of Physics, Engineering Physics & Astronomy, Queen's University, Kingston ON KL7 3N6, Canada
| | - Thomas Ferreira de Lima
- Department of Electrical Engineering, Princeton University, Princeton, NJ 08544, United States of America
| | - Paul Prucnal
- Department of Electrical Engineering, Princeton University, Princeton, NJ 08544, United States of America
| | - Alexander N Tait
- Physical Measurement Laboratory, National Institute of Standards and Technology (NIST), Boulder, CO 80305, United States of America
| | - Yichen Shen
- Lightelligence, 268 Summer Street, Boston, MA 02210, United States of America
| | - Huaiyu Meng
- Lightelligence, 268 Summer Street, Boston, MA 02210, United States of America
| | - Charles Roques-Carmes
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
| | - Zengguang Cheng
- Department of Materials, University of Oxford, Oxford OX1 3PH, United Kingdom
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, People's Republic of China
| | - Harish Bhaskaran
- Department of Materials, University of Oxford, Oxford OX1 3PH, United Kingdom
| | - Deep Jariwala
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia PA 19104, United States of America
| | - Han Wang
- University of Southern California, Los Angeles, CA 90089, United States of America
| | - Jeffrey M Shainline
- Physical Measurement Laboratory, National Institute of Standards and Technology (NIST), Boulder, CO 80305, United States of America
| | - Kenneth Segall
- Department of Physics and Astronomy, Colgate University, NY 13346, United States of America
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, United States of America
| | - Kaushik Roy
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, United States of America
| | - Suman Datta
- University of Notre Dame, Notre Dame, IN 46556, United States of America
| | - Arijit Raychowdhury
- Georgia Institute of Technology, Atlanta, GA 30332, United States of America
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8
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Marquez BA, Morison H, Guo Z, Filipovich M, Prucnal PR, Shastri BJ. Graphene-based photonic synapse for multi wavelength neural networks. ACTA ACUST UNITED AC 2020. [DOI: 10.1557/adv.2020.327] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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9
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Ma PY, Tait AN, Zhang W, Karahan EA, Ferreira de Lima T, Huang C, Shastri BJ, Prucnal PR. Blind source separation with integrated photonics and reduced dimensional statistics. Opt Lett 2020; 45:6494-6497. [PMID: 33258844 DOI: 10.1364/ol.409474] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 10/29/2020] [Indexed: 06/12/2023]
Abstract
Microwave communications have witnessed an incipient proliferation of multi-antenna and opportunistic technologies in the wake of an ever-growing demand for spectrum resources, while facing increasingly difficult network management over widespread channel interference and heterogeneous wireless broadcasting. Radio frequency (RF) blind source separation (BSS) is a powerful technique for demixing mixtures of unknown signals with minimal assumptions, but relies on frequency dependent RF electronics and prior knowledge of the target frequency band. We propose photonic BSS with unparalleled frequency agility supported by the tremendous bandwidths of photonic channels and devices. Specifically, our approach adopts an RF photonic front-end to process RF signals at various frequency bands within the same array of integrated microring resonators, and implements a novel two-step photonic BSS pipeline to reconstruct source identities from the reduced dimensional statistics of front-end output. We verify the feasibility and robustness of our approach by performing the first proof-of-concept photonic BSS experiments on mixed-over-the-air RF signals across multiple frequency bands. The proposed technique lays the groundwork for further research in interference cancellation, radio communications, and photonic information processing.
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10
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Ma PY, Tait AN, de Lima TF, Huang C, Shastri BJ, Prucnal PR. Photonic independent component analysis using an on-chip microring weight bank. Opt Express 2020; 28:1827-1844. [PMID: 32121887 DOI: 10.1364/oe.383603] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 12/27/2019] [Indexed: 06/10/2023]
Abstract
Independent component analysis (ICA) is a general-purpose technique for analyzing multi-dimensional data to reveal the underlying hidden factors that are maximally independent from each other. We report the first photonic ICA on mixtures of unknown signals by employing an on-chip microring (MRR) weight bank. The MRR weight bank performs so-called weighted addition (i.e., multiply-accumulate) operations on the received mixtures, and outputs a single reduced-dimensional representation of the signal of interest. We propose a novel ICA algorithm to recover independent components solely based on the statistical information of the weighted addition output, while remaining blind to not only the original sources but also the waveform information of the mixtures. We investigate both channel separability and near-far problems, and our two-channel photonic ICA experiment demonstrates our scheme holds comparable performance with the conventional software-based ICA method. Our numerical simulation validates the fidelity of the proposed approach, and studies noise effects to identify the operating regime of our method. The proposed technique could open new domains for future research in blind source separation, microwave photonics, and on-chip information processing.
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11
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Ma PY, Tait AN, de Lima TF, Abbaslou S, Shastri BJ, Prucnal PR. Photonic principal component analysis using an on-chip microring weight bank. Opt Express 2019; 27:18329-18342. [PMID: 31252778 DOI: 10.1364/oe.27.018329] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 06/05/2019] [Indexed: 06/09/2023]
Abstract
Photonic principal component analysis (PCA) enables high-performance dimensionality reduction in wideband analog systems. In this paper, we report a photonic PCA approach using an on-chip microring (MRR) weight bank to perform weighted addition operations on correlated wavelength-division multiplexed (WDM) inputs. We are able to configure the MRR weight bank with record-high accuracy and precision, and generate multi-channel correlated input signals in a controllable manner. We also consider the realistic scenario in which the PCA procedure remains blind to the waveforms of both the input signals and weighted addition output, and propose a novel PCA algorithm that is able to extract principal components (PCs) solely based on the statistical information of the weighted addition output. Our experimental demonstration of two-channel photonic PCA produces PCs holding consistently high correspondence to those computed by a conventional software-based PCA method. Our numerical simulation further validates that our scheme can be generalized to high-dimensional (up to but not limited to eight-channel) PCA with good convergence. The proposed technique could bring new solutions to problems in microwave communications, ultrafast control, and on-chip information processing.
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12
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George JK, Mehrabian A, Amin R, Meng J, de Lima TF, Tait AN, Shastri BJ, El-Ghazawi T, Prucnal PR, Sorger VJ. Neuromorphic photonics with electro-absorption modulators. Opt Express 2019; 27:5181-5191. [PMID: 30876120 DOI: 10.1364/oe.27.005181] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Photonic neural networks benefit from both the high-channel capacity and the wave nature of light acting as an effective weighting mechanism through linear optics. Incorporating a nonlinear activation function by using active integrated photonic components allows neural networks with multiple layers to be built monolithically, eliminating the need for energy and latency costs due to external conversion. Interferometer-based modulators, while popular in communications, have been shown to require more area than absorption-based modulators, resulting in a reduced neural network density. Here, we develop a model for absorption modulators in an electro-optic fully connected neural network, including noise, and compare the network's performance with the activation functions produced intrinsically by five types of absorption modulators. Our results show the quantum well absorption modulator-based electro-optic neuron has the best performance allowing for 96% prediction accuracy with 1.7×10-12 J/MAC excluding laser power when performing MNIST classification in a 2 hidden layer feed-forward photonic neural network.
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13
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Tait AN, Jayatilleka H, De Lima TF, Ma PY, Nahmias MA, Shastri BJ, Shekhar S, Chrostowski L, Prucnal PR. Feedback control for microring weight banks. Opt Express 2018; 26:26422-26443. [PMID: 30469730 DOI: 10.1364/oe.26.026422] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 07/22/2018] [Indexed: 06/09/2023]
Abstract
Microring weight banks present novel opportunities for reconfigurable, high-performance analog signal processing in photonics. Controlling microring filter response is a challenge due to fabrication variations and thermal sensitivity. Prior work showed continuous weight control of multiple wavelength-division multiplexed signals in a bank of microrings based on calibration and feedforward control. Other prior work has shown resonance locking based on feedback control by monitoring photoabsorption-induced changes in resistance across in-ring photoconductive heaters. In this work, we demonstrate continuous, multi-channel control of a microring weight bank with an effective 5.1 bits of accuracy on 2Gbps signals. Unlike resonance locking, the approach relies on an estimate of filter transmission versus photo-induced resistance changes. We introduce an estimate still capable of providing 4.2 bits of accuracy without any direct transmission measurements. Furthermore, we present a detailed characterization of this response for different values of carrier wavelength offset and power. Feedback weight control renders tractable the weight control problem in reconfigurable analog photonic networks.
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14
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Ma PY, Shastri BJ, Ferreira de Lima T, Huang C, Tait AN, Nahmias MA, Peng HT, Prucnal PR. Simultaneous excitatory and inhibitory dynamics in an excitable laser. Opt Lett 2018; 43:3802-3805. [PMID: 30067683 DOI: 10.1364/ol.43.003802] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 07/11/2018] [Indexed: 06/08/2023]
Abstract
Neocortical systems encode information in electrochemical spike timings, not just mean firing rates. Learning and memory in networks of spiking neurons is achieved by the precise timing of action potentials that induces synaptic strengthening (with excitation) or weakening (with inhibition). Inhibition should be incorporated into brain-inspired spike processing in the optical domain to enhance its information-processing capability. We demonstrate the simultaneous excitatory and inhibitory dynamics in an excitable (i.e., a pulsed) laser neuron, both numerically and experimentally. We investigate the bias strength effect, inhibitory strength effect, and excitatory and inhibitory input timing effect, based on the simulation platform of an integrated graphene excitable laser. We further corroborate these analyses with proof-of-principle experiments utilizing a fiber-based graphene excitable laser, where we introduce inhibition by directly modulating the gain of the laser. This technology may potentially open novel spike-processing functionality for future neuromorphic photonic systems.
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15
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Tait AN, Wu AX, Ferreira de Lima T, Nahmias MA, Shastri BJ, Prucnal PR. Two-pole microring weight banks. Opt Lett 2018; 43:2276-2279. [PMID: 29762571 DOI: 10.1364/ol.43.002276] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 04/07/2018] [Indexed: 06/08/2023]
Abstract
Weighted addition is an elemental multi-input to single-output operation that can be implemented with high-performance photonic devices. Microring (MRR) weight banks bring programmable weighted addition to silicon photonics. Prior work showed that their channel limits are affected by coherent inter-channel effects that occur uniquely in weight banks. We fabricate two-pole designs that exploit this inter-channel interference in a way that is robust to dynamic tuning and fabrication variation. Scaling analysis predicts a channel count improvement of 3.4-fold, which is substantially greater than predicted by incoherent analysis used in conventional MRR devices. Advances in weight bank design expand the potential of reconfigurable analog photonic networks and multivariate microwave photonics.
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16
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Jia C, Shastri BJ, Abdukerim N, Rochette M, Prucnal PR, Saad M, Chen LR. Passively synchronized Q-switched and mode-locked dual-band Tm 3+:ZBLAN fiber lasers using a common graphene saturable absorber. Sci Rep 2016; 6:36071. [PMID: 27804993 PMCID: PMC5090962 DOI: 10.1038/srep36071] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Accepted: 10/11/2016] [Indexed: 12/03/2022] Open
Abstract
Dual-band fiber lasers are emerging as a promising technology to penetrate new industrial and medical applications from their dual-band properties, in addition to providing compactness and environmental robustness from the waveguide structure. Here, we demonstrate the use of a common graphene saturable absorber and a single gain medium (Tm3+:ZBLAN fiber) to implement (1) a dual-band fiber ring laser with synchronized Q-switched pulses at wavelengths of 1480 nm and 1840 nm, and (2) a dual-band fiber linear laser with synchronized mode-locked pulses at wavelengths of 1480 nm and 1845 nm. Q-switched operation at 1480 nm and 1840 nm is achieved with a synchronized repetition rate from 20 kHz to 40.5 kHz. For synchronous mode-locked operation, pulses with full-width at half maximum durations of 610 fs and 1.68 ps at wavelengths of 1480 nm and 1845 nm, respectively, are obtained at a repetition rate of 12.3 MHz. These dual-band pulsed sources with an ultra-broadband wavelength separation of ~360 nm will add new capabilities in applications including optical sensing, spectroscopy, and communications.
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Affiliation(s)
- Chenglai Jia
- Department of Electrical and Computer Engineering, McGill University, Montréal, Québec H3A 0E9, Canada
| | - Bhavin J Shastri
- Department of Electrical Engineering, Princeton University, Princeton, NJ, 08544, USA
| | - Nurmemet Abdukerim
- Department of Electrical and Computer Engineering, McGill University, Montréal, Québec H3A 0E9, Canada
| | - Martin Rochette
- Department of Electrical and Computer Engineering, McGill University, Montréal, Québec H3A 0E9, Canada
| | - Paul R Prucnal
- Department of Electrical Engineering, Princeton University, Princeton, NJ, 08544, USA
| | - Mohammed Saad
- Thorlabs, Inc., 56 Sparta Ave., Newton, NJ, 07860, USA
| | - Lawrence R Chen
- Department of Electrical and Computer Engineering, McGill University, Montréal, Québec H3A 0E9, Canada
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17
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Tait AN, de Lima TF, Nahmias MA, Shastri BJ, Prucnal PR. Multi-channel control for microring weight banks. Opt Express 2016; 24:8895-8906. [PMID: 27137322 DOI: 10.1364/oe.24.008895] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
We demonstrate 4-channel, 2GHz weighted addition in a silicon microring filter bank. Accurate analog weight control becomes more difficult with increasing number of channels, N, as feedback approaches become impractical and brute force feedforward approaches take O(2N) calibration measurements in the presence of inter-channel dependence. We introduce model-based calibration techniques for thermal cross-talk and cross-gain saturation, which result in a scalable O(N) calibration routine and 3.8 bit feedforward weight accuracy on every channel. Practical calibration routines are indispensible for controlling large-scale microring systems. The effect of thermal model complexity on accuracy is discussed. Weighted addition based on silicon microrings can apply the strengths of photonic manufacturing, wideband information processing, and multiwavelength networks towards new paradigms of ultrafast analog distributed processing.
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18
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Shastri BJ, Nahmias MA, Tait AN, Rodriguez AW, Wu B, Prucnal PR. Spike processing with a graphene excitable laser. Sci Rep 2016; 6:19126. [PMID: 26753897 PMCID: PMC4709573 DOI: 10.1038/srep19126] [Citation(s) in RCA: 103] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Accepted: 12/07/2015] [Indexed: 12/22/2022] Open
Abstract
Novel materials and devices in photonics have the potential to revolutionize optical information processing, beyond conventional binary-logic approaches. Laser systems offer a rich repertoire of useful dynamical behaviors, including the excitable dynamics also found in the time-resolved "spiking" of neurons. Spiking reconciles the expressiveness and efficiency of analog processing with the robustness and scalability of digital processing. We demonstrate a unified platform for spike processing with a graphene-coupled laser system. We show that this platform can simultaneously exhibit logic-level restoration, cascadability and input-output isolation--fundamental challenges in optical information processing. We also implement low-level spike-processing tasks that are critical for higher level processing: temporal pattern detection and stable recurrent memory. We study these properties in the context of a fiber laser system and also propose and simulate an analogous integrated device. The addition of graphene leads to a number of advantages which stem from its unique properties, including high absorption and fast carrier relaxation. These could lead to significant speed and efficiency improvements in unconventional laser processing devices, and ongoing research on graphene microfabrication promises compatibility with integrated laser platforms.
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Affiliation(s)
- Bhavin J Shastri
- Department of Electrical Engineering, Princeton University, Princeton, New Jersey 08544, USA
| | - Mitchell A Nahmias
- Department of Electrical Engineering, Princeton University, Princeton, New Jersey 08544, USA
| | - Alexander N Tait
- Department of Electrical Engineering, Princeton University, Princeton, New Jersey 08544, USA
| | - Alejandro W Rodriguez
- Department of Electrical Engineering, Princeton University, Princeton, New Jersey 08544, USA
| | - Ben Wu
- Department of Electrical Engineering, Princeton University, Princeton, New Jersey 08544, USA
| | - Paul R Prucnal
- Department of Electrical Engineering, Princeton University, Princeton, New Jersey 08544, USA
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19
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Ma PY, Fok MP, Shastri BJ, Wu B, Prucnal PR. Gigabit Ethernet signal transmission using asynchronous optical code division multiple access. Opt Lett 2015; 40:5854-5857. [PMID: 26670529 DOI: 10.1364/ol.40.005854] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
We propose and experimentally demonstrate a novel architecture for interfacing and transmitting a Gigabit Ethernet (GbE) signal using asynchronous incoherent optical code division multiple access (OCDMA). This is the first such asynchronous incoherent OCDMA system carrying GbE data being demonstrated to be working among multi-users where each user is operating with an independent clock/data rate and is granted random access to the network. Three major components, the GbE interface, the OCDMA transmitter, and the OCDMA receiver are discussed in detail. The performance of the system is studied and characterized through measuring eye diagrams, bit-error rate and packet loss rate in real-time file transfer. Our Letter also addresses the near-far problem and realizes asynchronous transmission and detection of signal.
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20
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Nahmias MA, Tait AN, Shastri BJ, de Lima TF, Prucnal PR. Excitable laser processing network node in hybrid silicon: analysis and simulation. Opt Express 2015; 23:26800-26813. [PMID: 26480191 DOI: 10.1364/oe.23.026800] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The combination of ultrafast laser dynamics and dense on-chip multiwavelength networking could potentially address new domains of real-time signal processing that require both speed and complexity. We present a physically realistic optoelectronic simulation model of a circuit for dynamical laser neural networks and verify its behavior. We describe the physics, dynamics, and parasitics of one network node, which includes a bank of filters, a photodetector, and excitable laser. This unconventional circuit exhibits both cascadability and fan-in, critical properties for the large-scale networking of information processors based on laser excitability. In addition, it can be instantiated on a photonic integrated circuit platform and requires no off-chip optical I/O. Our proposed processing system could find use in emerging applications, including cognitive radio and low-latency control.
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21
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Tait AN, Chang J, Shastri BJ, Nahmias MA, Prucnal PR. Demonstration of WDM weighted addition for principal component analysis. Opt Express 2015; 23:12758-12765. [PMID: 26074530 DOI: 10.1364/oe.23.012758] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
We consider an optical technique for performing tunable weighted addition using wavelength-division multiplexed (WDM) inputs, the enabling function of a recently proposed photonic spike processing architecture [J. Lightwave Technol., 32 (2014)]. WDM weighted addition provides important advantages to performance, integrability, and networking capability that were not possible in any past approaches to optical neurocomputing. In this letter, we report a WDM weighted addition prototype used to find the first principal component of a 1Gbps, 8-channel signal. Wideband, multivariate techniques have immediate relevance to modern radio systems, and photonic spike processing networks enabled by WDM could open new domains of information processing that bring unprecedented bandwidth and intelligence to problems in radio communications, ultrafast control, and scientific computing.
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22
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Nahmias MA, Shastri BJ, Tait AN, Eder M, Rafidi N, Tian Y, Prucnal PR. Normalized pulsed energy thresholding in a nonlinear optical loop mirror. Appl Opt 2015; 54:3218-3224. [PMID: 25967306 DOI: 10.1364/ao.54.003218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
We demonstrate for the first time, to the best of our knowledge, that a Sagnac interferometer can threshold the energies of pulses. Pulses below a given threshold T are suppressed, while those above this threshold are normalized. The device contains an in-loop tunable isolator and 10.4 m of a highly doped silica fiber. We derive an analytical model of the nonlinear optical loop mirror's pulse energy transfer function and show that its energy transfer function approximates a step function for very high phase shifts (>π). We reveal some limitations of this approach, showing that a step-function transfer function necessarily results in pulse distortion in fast, nonresonant all-optical devices.
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23
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Shastri BJ, Nahmias MA, Tait AN, Wu B, Prucnal PR. SIMPEL: circuit model for photonic spike processing laser neurons. Opt Express 2015; 23:8029-8044. [PMID: 25837141 DOI: 10.1364/oe.23.008029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
We propose an equivalent circuit model for photonic spike processing laser neurons with an embedded saturable absorber—a simulation model for photonic excitable lasers (SIMPEL). We show that by mapping the laser neuron rate equations into a circuit model, SPICE analysis can be used as an efficient and accurate engine for numerical calculations, capable of generalization to a variety of different types of laser neurons with saturable absorber found in literature. The development of this model parallels the Hodgkin-Huxley model of neuron biophysics, a circuit framework which brought efficiency, modularity, and generalizability to the study of neural dynamics. We employ the model to study various signal-processing effects such as excitability with excitatory and inhibitory pulses, binary all-or-nothing response, and bistable dynamics.
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24
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Wu B, Chang MP, Shastri BJ, Wang Z, Prucnal PR. Analog noise protected optical encryption with two-dimensional key space. Opt Express 2014; 22:14568-14574. [PMID: 24977552 DOI: 10.1364/oe.22.014568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
An optical encryption method based on analog noise is proposed and experimentally demonstrated. The transmitted data is encrypted with wideband analog noise. Without decrypting the data instantly at the receiver, the data is damaged by the noise and cannot be recovered by post-processing techniques. A matching condition in both phase and amplitude of the noise needs to be satisfied between the transmitter and the receiver to cancel the noise. The precise requirement of the phase and amplitude matching condition provides a large two-dimensional key space, which can be deployed in the encryption and decryption process at the transmitter and receiver.
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25
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Wu B, Wang Z, Shastri BJ, Chang MP, Frost NA, Prucnal PR. Temporal phase mask encrypted optical steganography carried by amplified spontaneous emission noise. Opt Express 2014; 22:954-961. [PMID: 24515055 DOI: 10.1364/oe.22.000954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
A temporal phase mask encryption method is proposed and experimentally demonstrated to improve the security of the stealth channel in an optical steganography system. The stealth channel is protected in two levels. In the first level, the data is carried by amplified spontaneous emission (ASE) noise, which cannot be detected in either the time domain or spectral domain. In the second level, even if the eavesdropper suspects the existence of the stealth channel, each data bit is covered by a fast changing phase mask. The phase mask code is always combined with the wide band noise from ASE. Without knowing the right phase mask code to recover the stealth data, the eavesdropper can only receive the noise like signal with randomized phase.
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26
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Tait AN, Nahmias MA, Tian Y, Shastri BJ, Prucnal PR. Photonic Neuromorphic Signal Processing and Computing. Nanophotonic Information Physics 2014. [DOI: 10.1007/978-3-642-40224-1_8] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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27
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Wu B, Wang Z, Tian Y, Fok MP, Shastri BJ, Kanoff DR, Prucnal PR. Optical steganography based on amplified spontaneous emission noise. Opt Express 2013; 21:2065-2071. [PMID: 23389187 DOI: 10.1364/oe.21.002065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
We propose and experimentally demonstrate an optical steganography method in which a data signal is transmitted using amplified spontaneous emission (ASE) noise as a carrier. The ASE serving as a carrier for the private signal has an identical frequency spectrum to the existing noise generated by the Erbium doped fiber amplifiers (EDFAs) in the transmission system. The system also carries a conventional data channel that is not private. The so-called "stealth" or private channel is well-hidden within the noise of the system. Phase modulation is used for both the stealth channel and the public channel. Using homodyne detection, the short coherence length of the ASE ensures that the stealth signal can only be recovered if the receiver closely matches the delay-length difference, which is deliberately changed in a dynamic fashion that is only known to the transmitter and its intended receiver.
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Affiliation(s)
- Ben Wu
- Lightwave Communications Laboratory, Department of Electrical Engineering, Princeton University, Princeton, New Jersey 08544, USA.
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