Reconfigurable binary diffractive optical neural network based on chalcogenide phase change material Ge2Sb2Se4Te1

被引:2
作者
Fu, Ziwei [1 ,2 ,3 ]
Fu, Tingzhao [1 ,2 ,3 ]
Wu, Hao [1 ,2 ,3 ]
Zhu, Zhihong [1 ,2 ,3 ]
Zhang, Jianfa [1 ,2 ,3 ]
机构
[1] Natl Univ Def Technol, Coll Adv Interdisciplinary Studies, Changsha 410073, Hunan, Peoples R China
[2] Natl Univ Def Technol, Hunan Prov Key Lab Novel NanoOptoelect Informat Ma, Changsha 410073, Hunan, Peoples R China
[3] Natl Univ Def Technol, Nanhu Laser Lab, Changsha 410073, Peoples R China
来源
OPTICS EXPRESS | 2024年 / 32卷 / 23期
基金
中国国家自然科学基金;
关键词
Binary training - Low energy consumption - Network frameworks - Network-based - Nonvolatile - Optical neural networks - Parallel processing - Phase Change - Reconfigurable - Training algorithms;
D O I
10.1364/OE.539235
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Diffractive optical neural networks (DONNs) possess unique advantages such as light-speed computing, low energy consumption, and parallel processing, which have obtained increasing attention in recent years. However, once conventional DONNs are fabricated, their function remains fixed, which greatly limits the applications of DONNs. Thus, we propose a reconfigurable DONN framework based on a repeatable and non-volatile phase change material Ge2Sb2Se4Te1(GSST). By utilizing phase modulation units made of GSST to form the network's neurons, we can flexibly switch the functions of the DONN. Meanwhile, we apply a binary training algorithm to train the DONN weights to binary values of 0 and pi , which is beneficial for simplifying the design and fabrication of DONN while reducing errors during physical implementation. Furthermore, the reconfigurable binary DONN has been trained as a handwritten digit classifier and a fashion product classifier to validate the feasibility of the framework. This work provides an efficient and flexible control mechanism for reconfigurable DONNs, with potential applications in various complex tasks. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
引用
收藏
页码:41433 / 41444
页数:12
相关论文
共 48 条
[1]   Tunable nanophotonics enabled by chalcogenide phase-change materials [J].
Abdollahramezani, Sajjad ;
Hemmatyar, Omid ;
Taghinejad, Hossein ;
Krasnok, Alex ;
Kiarashinejad, Yashar ;
Zandehshahvar, Mohammadreza ;
Alu, Andrea ;
Adibi, Ali .
NANOPHOTONICS, 2020, 9 (05) :1189-1241
[2]   Thermally-Induced Degradation in PM6:Y6-Based Bulk Heterojunction Organic Solar Cells [J].
Alam, Shahidul ;
Aldosari, Haya ;
Petoukhoff, Christopher E. ;
Vary, Tomas ;
Althobaiti, Wejdan ;
Alqurashi, Maryam ;
Tang, Hua ;
Khan, Jafar I. ;
Nadazdy, Vojtech ;
Mueller-Buschbaum, Peter ;
Welch, Gregory C. ;
Laquai, Frederic .
ADVANCED FUNCTIONAL MATERIALS, 2024, 34 (06)
[3]   LOCAL-FIELD EFFECTS AND EFFECTIVE-MEDIUM THEORY - A MICROSCOPIC PERSPECTIVE [J].
ASPNES, DE .
AMERICAN JOURNAL OF PHYSICS, 1982, 50 (08) :704-709
[4]  
Chen H., 2024, Appl. Phys. Rev, V11, P1
[5]   Diffractive Deep Neural Networks at Visible Wavelengths [J].
Chen, Hang ;
Feng, Jianan ;
Jiang, Minwei ;
Wang, Yiqun ;
Lin, Jie ;
Tan, Jiubin ;
Jin, Peng .
ENGINEERING, 2021, 7 (10) :1483-1491
[6]   Optical multi-task learning using multi-wavelength diffractive deep neural networks [J].
Duan, Zhengyang ;
Chen, Hang ;
Lin, Xing .
NANOPHOTONICS, 2023, 12 (05) :893-903
[7]   Parallel convolutional processing using an integrated photonic tensor core [J].
Feldmann, J. ;
Youngblood, N. ;
Karpov, M. ;
Gehring, H. ;
Li, X. ;
Stappers, M. ;
Le Gallo, M. ;
Fu, X. ;
Lukashchuk, A. ;
Raja, A. S. ;
Liu, J. ;
Wright, C. D. ;
Sebastian, A. ;
Kippenberg, T. J. ;
Pernice, W. H. P. ;
Bhaskaran, H. .
NATURE, 2021, 589 (7840) :52-+
[8]   All-optical spiking neurosynaptic networks with self-learning capabilities [J].
Feldmann, J. ;
Youngblood, N. ;
Wright, C. D. ;
Bhaskaran, H. ;
Pernice, W. H. P. .
NATURE, 2019, 569 (7755) :208-+
[9]   Multi-wavelength diffractive neural network with the weighting method [J].
Feng, Jianan ;
Chen, Hang ;
Yang, Dahai ;
Hao, Junbo ;
Lin, Jie ;
Jin, Peng .
OPTICS EXPRESS, 2023, 31 (20) :33113-33122
[10]   Optical neural networks: progress and challenges [J].
Fu, Tingzhao ;
Zhang, Jianfa ;
Sun, Run ;
Huang, Yuyao ;
Xu, Wei ;
Yang, Sigang ;
Zhu, Zhihong ;
Chen, Hongwei .
LIGHT-SCIENCE & APPLICATIONS, 2024, 13 (01)