硅基集成衍射光学神经网络研究（英文版）

正版全新

54.82 6.9折 79 全新

库存11件

上海浦东

出版社清华大学出版社

ISBN9787302691419

出版时间2022-12

装帧其他

定价79元

货号5016419

上书时间2026-07-28

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品相描述：全新

商品描述: 【书    名】硅基集成衍射光学神经网络研究（英文版）
【书    号】 9787302691419
【出版社】清华大学出版社
【出版日期】 2026-03-01
【定    价】 79.00元

【编辑推荐】
《硅基集成衍射光学神经网络研究（英文版）》针对硅基集成衍射光学神经网络展开系统研究，取得了一系列研究成果。

【内容简介】
《硅基集成衍射光学神经网络研究（英文版）》针对硅基集成衍射光学神经网络（DONN）高集成度、高计算容量以及计算单元大规模拓展等关键科学问题展开系统研究。具体包括硅基集成DONN的理论探索、结构设计、仿真计算以及芯片制造、封装、测试和实验系统误差补偿等方面工作。硅基集成DONN芯片具有高集成度、高计算容量、计算单元易于大规模拓展以及稳定性、便携性好等优势，是未来突破算力瓶颈的有效方案之一。《硅基集成衍射光学神经网络研究（英文版）》将对从事硅基集成光子芯片相关研究的科研工作者提供有价值的参考。

【目录】
Contents

1Research Background and Progress1

1.1Introduction1

1.2Research on Non-integrated ONNs3

1.2.1Non-integrated Free-Space Diffractive ONNs3

1.2.2Non-integrated Lens-Based ONNs5

1.2.3Other Non-integrated Spatial Bulk ONNs5

1.3Research on Integrated ONNs6

1.3.1Silicon-Based Integrated Interferometric ONNs6

1.3.2Silicon-Based Integrated WDM-System ONNs7

1.3.3Silicon-Based Integrated Diffractive ONNs8

1.3.4Other Integrated ONNs8

1.4Research Significance and Objectives9

1.5Main Content and Structure of the Thesis10

References12

2Silicon-Based Integrated Diffractive Optical Neural

Network Model17

2.1Two-Dimensional Spatial Domain Electromagnetic

Propagation Model17

2.2Diffractive Optical Neural Network Training Model25

2.3Phase Weight Mapping Model30

2.4Summary41

References41

3Silicon-Based Integrated Diffractive Optical Neural

Network Chip43

3.1Binary Classification Silicon-Based Integrated DONN Chip43

3.1.1Design of Structures44

3.1.2Simulation Verification46

3.1.3Performance Analysis49

XIVContents

3.2Ternary Classification Silicon-Based Integrated DONN Chip51

3.2.1Design of Structures51

3.2.2Simulation Verification56

3.2.3Experimental Tests and Results Analysis56

3.2.4System Error Compensation63

3.2.5Performance Analysis73

3.3Ten Classification Silicon-Based Integrated DONN Chip74

3.3.1Design of Structures75

3.3.2Experimental Tests and Results Analysis77

3.3.3Performance Analysis80

3.4Summary82

References84

4Research on Multifunctional Silicon-Based Integrated

Diffractive Optical Neural Network85

4.1Network Architecture85

4.2Serial-to-Parallel Conversion Technology Based on Arrayed

Waveguide Structure86

4.2.1Structure Design of Arrayed Waveguides87

4.2.2Experimental Testing of the Arrayed Waveguide

Structure89

4.2.3Silicon-Based Integrated Diffractive Optical Neural

Network with Serial-to-Parallel Conversion91

4.3Multi-task Silicon-Based Integrated Diffractive Optical

Neural Network97

4.3.1Multi-task Learning97

4.3.2Transfer Learning99

4.3.3Design of Structures99

4.3.4Simulation Verification103

4.4Summary104

References105

5Conclusions and Future Perspectives107

5.1Conclusions of Research Work107

5.2Future Perspectives of Research Work109

References111

【前言】
清华大学优秀博士学位论文丛书

硅基集成衍射光学神经网络研究（英文版）

Research on Silicon-Based Integrated Diffractive Optical Neural Network

符庭钊（Fu Tingzhao）著

北京

For sale and distribution in the mainland of the People’s Republic of China exclusively.

此版本仅限于中国大陆地区销售。本书海外版由清华大学出版社授权 Springer在中国大陆以外地区出版发行： ISBN 978-981-95-3786-0。

版权所有，侵权必究。举报：010-62782989，beiqinquan@tup.tsinghua.edu.cn。

图书在版编目 (CIP) 数据

硅基集成衍射光学神经网络研究 : 英文 / 符庭钊著. -- 北京 : 清华大学出版社, 2026. 3. -- (清华大学优秀博士学位论文丛书). ISBN 978-7-302-69141-9

Ⅰ. TN304.2
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出版发行：清华大学出版社网址：https://www.tup.com.cn, https://www.wqxuetang.com 地址：北京清华大学学研大厦 A座邮编：100084 社总机：010-83470000 邮购：010-62786544 投稿与读者服务：010-62776969, c-service@tup.tsinghua.edu.cn 质量反馈：010-62772015, zhiliang@tup.tsinghua.edu.cn

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Supervisor’s Foreword
Arti.cial intelligence is sweeping across the globe at an unprecedented pace, yet the underlying computational architecture that supports its development faces dual challenges of computing power and power consumption. Optical neural networks represent a highly forward-looking computational architecture, boasting signi.cant advantages such as ultra-high speed, low-power consumption, and large bandwidth. Currently, there has been extensive research in areas such as image processing and speech recognition, positioning it as a strong contender for the next generation of computational architectures. Among these, diffractive optical neural networks (DONNs), characterized by their high integration and high computing capacity, hold the promise of serving as a bridge for the perfect combination of optical computing and arti.cial intelligence.

This book systematically investigates the fundamental principles, design method, simulation, and experimental validation of on-chip integrated DONNs, covering the complete chain from theoretical foundations to practical veri.cation. The main inno-vations include: . A parameter training and mapping model for on-chip DONNs is proposed, which can analytically characterize the propagation laws of light in silicon-based slab waveguides and enable more accurate implementation of pre-trained key parameters on physical devices; . A silicon-based integrated DONN chip with low latency, low-power consumption, high integration, and high computing capacity is proposed, along with a compensation scheme for system errors in the chip, further ensuring the performance of the DONN chip system; . A design methodology for a multifunctional silicon-based integrated DONN chip system is proposed, which further enhances the computing ef.ciency of the DONN chip.

This research topic holds important theoretical signi.cance and practical value. Most of the content covered in this book has previously been published in the foremost journals or conferences within this research .eld. Despite the rapid development in

Supervisor’s Foreword

optical neural networks, understanding the fundamental technologies and principles of on-chip DONNs can provide readers with a comprehensive, systematic, and in-depth reference. For readers engaged in interdisciplinary research in optics, electronic information, arti.cial intelligence, and related .elds, this book may help them apply similar techniques or inspire them to develop novel solutions.

Beijing, China Hongwei Chen March 2025

Related Publications in This Thesis

[1] Fu T, Zang Y, Huang Y, et al. Photonic machine learning with on-chip diffractive optics[J]. Nature Communications, 2023, 14(1): 70

[2] Fu T, Zang Y, Huang H, et al. On-chip photonic diffractive optical neural network based on a spatial domain electromagnetic propagation model[J]. Optics Express, 2021, 29(20): 31924–31940

[3] Fu T, Huang Y, Sun R, et al. Integrated diffractive optical neural network with space-time interleaving[J]. Chinese Optics Letters, 2023, 21(9): 091301

Acronyms
2.5D FDTD  2.5-Dimensional Variational FDTD

AM  Amplitude Modulator

ASIC  Application-Speci.c Integrated Circuit

AWG  Arbitrary Waveform Generator

CMOS  Complementary Metal Oxide Semiconductor

CPU  Central Processing Unit

DMD  Digital Micro-mirror Device

DONN  Diffractive Optical Neural Network

EMT  Effective Medium Theory

FA  Fiber Array

Fashion-MNIST  Fashion-Modi.ed National Institute of Standards and

Technology Database

FDTD  Finite Difference Time Domain

FPGA  Field-Programmable Gate Array

MMI  Multimode Interferometer

MNIST  Modi.ed National Institute of Standards and Technology

Database

ONN  Optical Neural Network

OPS  Operations per Second

PCB  Printed Circuit Board

PD  Photodetector

SDEPM  Spatial Domain Electromagnetic Propagation Model

SiO2  Silicon Dioxide/Silica

SLM  Spatial Light Modulator

SOI  Silicon-on-Insulator

SSSD  Silicon Slot Filled with Silicon Dioxide

SSSDG  Silicon Slot Filled with Silicon Dioxide Group

TOPS  Trillions of Operations per Second

USPS  United States Postal Service

Contents
1 Research Background and Progress .............................. 1
1.1 Introduction ............................................... 1
1.2 ResearchonNon-integratedONNs ............................ 3
1.2.1 Non-integrated Free-Space Diffractive ONNs . . . . . . . . . . . . 3

1.2.2 Non-integratedLens-BasedONNs ...................... 5
1.2.3 OtherNon-integratedSpatialBulkONNs ................ 5
1.3 ResearchonIntegratedONNs ................................ 6
1.3.1 Silicon-Based Integrated Interferometric ONNs . . . . . . . . . . 6

1.3.2 Silicon-Based Integrated WDM-System ONNs . . . . . . . . . . . 7

1.3.3 Silicon-Based Integrated Diffractive ONNs . . . . . . . . . . . . . . 8

1.3.4 OtherIntegratedONNs ............................... 8
1.4 ResearchSigni.canceandObjectives ......................... 9
1.5 MainContentandStructureoftheThesis ...................... 10
References ..................................................... 12
2 Silicon-Based Integrated Diffractive Optical Neural Network Model ................................................ 17

2.1 Two-Dimensional Spatial Domain Electromagnetic PropagationModel .......................