Helps readers understand the important advances in nanophotonics materials development and their latest applications
This book introduces the current state of and emerging trends in the development of integrated nanophotonics. Written by three well-qualified authors, it systematically reviews the knowledge of integrated nanophotonics from theory to the most recent technological developments. It also covers the applications of integrated nanophotonics in essential areas such as neuromorphic computing, biosensing, and optical communications. Lastly, it brings together the latest advancements in the key principles of photonic integrated circuits, plus the recent advances in tackling the barriers in photonic integrated circuits.
Sample topics included in this comprehensive resource include: - Platforms for integrated nanophotonics, including lithium niobate nanophotonics, indium phosphide nanophotonics, silicon nanophotonics, and nonlinear optics for integrated photonics - The devices and technologies for integrated nanophotonics in on-chip light sources, optical packaging of photonic integrated circuits, optical interconnects, and light processing devices - Applications on neuromorphic computing, biosensing, LIDAR, and computing for AI and artificial neural network and deep learning
Materials scientists, physicists, and physical chemists can use this book to understand the totality of cutting-edge theory, research, and applications in the field of integrated nanophotonics.
Table of Contents
Preface xi
1 Packaging and Test of Photonic Integrated Circuits (PICs) 1
Stéphane Bernabé, Tolga Tekin, Bogdan Sirbu, Jean Charbonnier, Philippe Grosse, and Moritz Seyfried
1.1 Introduction 1
1.2 Challenges and Specificities of PIC Packaging and Test 2
1.2.1 Optical Interconnects 3
1.2.2 Coupling Structures 5
1.2.2.1 Edge Coupler 5
1.2.2.2 Vertical Grating Coupler (VGC) 6
1.2.2.3 Evanescent Coupling 7
1.2.3 Wafer-level Test 7
1.2.4 Module Packaging 10
1.2.5 Fiber Optic Assembly (Pigtailing) 12
1.2.5.1 PIC Alignment to a Lensed Fiber 12
1.2.5.2 PIC Butt Coupling to a Standard Cleaved Single-mode Fiber 12
1.2.5.3 Lens Coupling Scheme 13
1.2.5.4 Optical Waveguide Interposer Coupling 14
1.2.6 Emerging Trends for Module Mass Manufacturing 15
1.3 Advances in Optical Coupling Strategies 18
1.3.1 Toward Passive Alignment Strategies 19
1.3.2 Advanced Technologies for Vision-Assisted Technologies 20
1.3.2.1 Open-Loop Alignment 20
1.3.2.2 Closed-Loop Alignment 20
1.3.3 Advanced Technologies for Self-alignment Strategies 21
1.3.3.1 Self-alignment of Fiber to PIC Through an Silicon Optical Bench Using Flip-Chip 22
1.3.3.2 Self-alignment-assisted Microlenses Assembly 22
1.3.3.3 Self-alignment of Polymer Waveguides 22
1.3.3.4 Self-alignment of Optical Plug 23
1.3.4 Laser/PIC Coupling 23
1.4 Electronic/Photonic Convergence 25
1.4.1 Flip-chip Interconnects 26
1.4.1.1 Standard Die-to-die interconnects 26
1.4.1.2 Advanced Interconnects for Future Needs 27
1.4.2 Intra-connections (Through Silicon Vias and Through Glass Vias) 29
1.4.2.1 TSV Last Process 29
1.4.2.2 TSV Middle Process 30
1.4.2.3 Through Glass Via (TGV) 31
1.4.3 Fan-out Wafer-level Packaging (FOWLP) 31
1.4.4 Interposers Integration Approach 32
1.4.4.1 Interposers for Electronic Integrated Circuits (CMOS) 33
1.4.4.2 Photonic Interposer and Photonic Systems on Chip 34
1.5 Toward an Ecosystem in Test and Assembly of PICs 36
1.5.1 Design Rules for Packaging and Test 36
1.5.1.1 3D Packaging 38
1.5.1.2 Design Rules for Testing 39
1.5.2 Advanced Techniques for Wafer-level Test 39
1.5.3 Recent Achievements and Future Aspects in Assembly Machines 40
1.6 Conclusion 45
Acknowledgments 46
References 46
2 The Last Mile Technology of Silicon Photonics Toward Productions and Emerging Applications 53
Bo Li, Shawn Yohanes Siew, Feng Gao, Shawn Wu Xie, Qiang Li, Chao Li, Xianshu Luo, Guo-Qiang Lo, and Junfeng Song
2.1 Introduction 53
2.2 Fiber-to-Chip Assembly 55
2.3 Hybrid Integration of Light Source 59
2.4 Electronic and Photonic Co-Packaging 63
2.5 Outlook 65
2.5.1 Silicon Photonics Emerging Applications 65
2.5.2 Opportunities and Challenges 68
References 70
3 Integrated Nonlinear Photonics and Emerging Applications 75
Yang Yue, Wenpu Geng, Yuxi Fang, and Yingning Wang
3.1 Introduction 75
3.2 Supercontinuum 77
3.2.1 Applications 77
3.2.2 History of SCG in Integrated Waveguides 79
3.2.3 Representative Works 83
3.3 Optical Frequency Comb 90
3.3.1 Microresonator-Based OFC 91
3.3.2 SC-Based OFC 99
3.3.3 EO-Based OFC 99
3.3.4 MLL-Based OFC 99
3.3.5 Applications 101
3.4 Nonlinear Wave Mixing 102
3.4.1 Introduction 102
3.4.2 Nonlinear Optical Signal Processing in Integrated Waveguides 105
3.4.3 Representative Works 108
3.5 Conclusion and Perspectives 116
References 117
4 Excitation, Generation, Positioning, and Modulation for Quantum Light Sources Integrated on Chip 135
Cuo Wu, Cuiping Ma, and Zhiming Wang
4.1 Introduction 135
4.2 Excitation and Orientation of Quantum Emitters 136
4.3 Chip-Scale Integration Based on Quantum Emitters 141
4.3.1 Solution-Based Colloidal and Self-Assembled Quantum Dots 141
4.3.2 Strain-Induced Emitter Sites of Two-Dimensional Materials 144
4.3.3 Color Centers in Nanodiamond 148
4.4 Deterministically Positioning of Quantum Emitter 154
4.5 Quantum Light Interaction with Metasurface for Modulation 156
4.6 Conclusion 159
References 160
5 Quantum Light Sources in Two-Dimensional Materials 167
Yanan Wang and Philip X.-L. Feng
5.1 Introduction 167
5.2 Theory of Quantum Light Sources 168
5.2.1 Photon Statistics 168
5.2.1.1 Thermal Light 169
5.2.1.2 Coherent Light 170
5.2.1.3 Squeezed Light 170
5.2.2 Characteristics of Quantum Light Sources 172
5.2.2.1 Wavelength 172
5.2.2.2 Lifetime, Emission Rate, and Brightness 172
5.2.2.3 Emission Linewidth 173
5.2.2.4 Zero-Phonon Line (ZPL) and Debye-Waller Factor 173
5.2.2.5 Photon Polarization and Dipole Orientation 173
5.2.2.6 Optically Addressable Spin State 174
5.2.2.7 Indistinguishability 174
5.3 Quantum Light Sources in 2D Materials 175
5.3.1 Localized Excitons in Transition Metal Dichalcogenides 176
5.3.2 Defect Centers in Hexagonal Boron Nitride 179
5.3.3 Graphene Quantum Dots 183
5.3.4 Quantum Light-Emitting Diodes 186
5.4 Integration with On-Chip Components 189
5.4.1 Theory of SPE-Cavity Coupling 190
5.4.1.1 Strong Coupling Regime 190
5.4.1.2 Weak Coupling Regime 191
5.4.2 Integration with Dielectric Waveguides and Cavities 191
5.4.2.1 Transferring 2D SPEs onto Predefined Structures 192
5.4.2.2 Transferring or Fabricating Photonic Structures on 2D Materials 194
5.4.2.3 Monolithic Integration 195
5.4.3 Integration with Plasmonic Waveguides and Cavities 197
5.5 Integration with Off-Chip Components 199
5.5.1 Flip-chip Integration 199
5.5.2 Integration with Optic Fibers 200
5.6 Summary and Outlook 202
Acknowledgments 203
References 204
6 Inverse Design for Integrated Photonics Using Deep Neural Network 209
Keisuke Kojima, Toshiaki Koike-Akino, Yingheng Tang, and Ye Wang
6.1 Introduction 209
6.2 Deep Neural Network (DNN) Models 210
6.2.1 Forward Modeling 211
6.2.2 Inverse Modeling 212
6.2.3 Generative Modeling 212
6.3 Deep Learning for Forward Modeling to Predict Optical Response 212
6.4 Deep Learning for Inverse Modeling to Construct Device Topology 217
6.5 Deep Learning for Generative Modeling to Produce Device Topology Candidates 220
6.6 Physics-informed Neural Networks 225
6.7 Nanophotonic Power Splitter Design Using Generative Modeling 227
6.7.1 Device Structure 228
6.7.2 Device Simulation Procedure 229
6.7.3 Network Architecture 230
6.7.4 Network Training Procedure 231
6.7.5 Device Generation Performance 232
6.7.6 Hyperparameters 234
6.7.7 Adjoint Method vs. Deep Learning 234
6.8 Deep Learning Techniques 235
6.8.1 Convolutional Neural Networks 235
6.8.2 Transfer Learning and Fine Tuning 235
6.8.3 AutoML: Meta Learning, Learning to Learn, Network Architecture Search 236
6.9 Conclusion 237
References 237
7 Deep Learning Driven Data Processing, Modeling, and Inverse Design for Nanophotonics 245
Peter R. Wiecha, Nicholas J. Dinsdale, and Otto L. Muskens
7.1 Introduction 245
7.2 Artificial Neural Networks and Deep Learning 245
7.2.1 Artificial Neurons and Neural Networks 246
7.2.2 Training of Artificial Neural Networks 247
7.3 Ultrafast Physics Predictions 248
7.3.1 Specialized Physics Predictors: Fully Connected vs. Convolutional ANNs 249
7.3.2 Generalized Nanophotonics Predictor Network 252
7.4 Photonics Inverse Design 255
7.4.1 Predictor Network as a Surrogate Model for Optimization 256
7.4.1.1 Example: Polarization Conversion Maximization 257
7.4.1.2 Example: Maximize Magnetic Near-Field 258
7.4.2 Direct Inverse Design Networks 259
7.4.3 Optimizing Inverse Design Performance 260
7.4.3.1 Optimizing the Network Layout 262
7.4.3.2 Quality of the Initial Dataset 262
7.4.3.3 Iterative Training 264
7.4.3.4 Postprocessing 265
7.5 Advanced Data Processing for Photonics Applications 265
7.5.1 Optical Data Storage below the Diffraction Limit 265
7.5.2 Speckle Reconstruction for Real-time Hyperspectral Imaging 267
7.6 Conclusion and Outlook 269
References 270
8 Optical Waveguide of Lithium Niobate Nanophotonics 277
Yarub Al-Douri
8.1 Introduction 277
8.2 Photonics Lithium Niobate 278
8.3 Nanophotonic Lithium Niobate-Based Optical Waveguide 286
8.4 Optical Studies of Nanophotonic Lithium Niobate-Based Optical Waveguide 287
8.5 Nanophotonic LiNbO 3 Under Stirrer Time Effect 295
8.6 Nanophotonic Studies of LiNbO 3 Under Stirrer Time Effect 297
8.7 Conclusions 304
References 305
9 Active, Tunable, and Reconfigurable Nanophotonics 313
Trevon Badloe, Jaehyuck Jang, Heonyeong Jeong, Minsu Jeong, Inki Kim, Byoungsu Ko, Jihae Lee, Taejun Lee, Seong-Won Moon, Dong Kyo Oh, Younghwan Yang, Gwanho Yoon, and Junsuk Rho
9.1 Introduction 313
9.2 Liquid Crystal-Integrated Tunable Devices 314
9.2.1 Devices that Modulate Polarization 314
9.2.2 Devices that Modulate Effective Refractive Index 316
9.3 Optically Tunable Devices 318
9.3.1 Devices that Are Dependent on the Direction of Incident Light 318
9.3.2 Devices that Depend on Wavelength 319
9.3.3 Devices that Depend on Polarization (Spin) 321
9.3.4 Orbital Angular Momentum-dependent Devices 323
9.4 Phase Change Materials-Based Reconfigurable Devices 324
9.4.1 Switchable Absorbers 324
9.4.2 Thermochromic Smart Windows 327
9.5 Mechanically Tunable Photonic Devices 329
9.5.1 Tunable Devices that Use Micro-electro-mechanical Systems 329
9.5.2 Photonic Devices that Are Tuned Using Strain 331
9.6 Tunable Photonic Devices with Material Engineering 335
9.6.1 Bandgap Engineering for Tunable Solid-state Devices 335
9.6.2 Biomaterials for Tunable Biophotonic Devices 339
9.7 Electrically Tunable Photonic Devices 341
Acknowledgments 346
References 346
Index 359
