Integrated Nanophotonics

Peng Yu received his M.S. and PhD. degrees from University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2015 and 2018, respectively. His current research interests include plasmon-enhanced light-matter interactions and thermoplasmonics.Hongxing Xu’s main areas of research are plasmonics, nanophotonics, single-molecule surface-enhanced Raman spectroscopy, and integrated nanophotonic devices. He is one of the pioneers in single-molecule surface-enhanced Raman spectroscopy (SERS) and plasmonics.Zhiming Wang is currently a professor at the University of Electronic Science and Technology of China. He has many years of research experience in the molecular beam epitaxial growth and characterization of compound semiconductor nanomaterials and the design and preparation of optoelectronic prototype devices.

• Preface xi1 Packaging and Test of Photonic Integrated Circuits (PICs) 1Stéphane Bernabé, Tolga Tekin, Bogdan Sirbu, Jean Charbonnier, Philippe Grosse, and Moritz Seyfried1.1 Introduction 11.2 Challenges and Specificities of PIC Packaging and Test 21.2.1 Optical Interconnects 31.2.2 Coupling Structures 51.2.2.1 Edge Coupler 51.2.2.2 Vertical Grating Coupler (VGC) 61.2.2.3 Evanescent Coupling 71.2.3 Wafer-level Test 71.2.4 Module Packaging 101.2.5 Fiber Optic Assembly (Pigtailing) 121.2.5.1 PIC Alignment to a Lensed Fiber 121.2.5.2 PIC Butt Coupling to a Standard Cleaved Single-mode Fiber 121.2.5.3 Lens Coupling Scheme 131.2.5.4 Optical Waveguide Interposer Coupling 141.2.6 Emerging Trends for Module Mass Manufacturing 151.3 Advances in Optical Coupling Strategies 181.3.1 Toward Passive Alignment Strategies 191.3.2 Advanced Technologies for Vision-Assisted Technologies 201.3.2.1 Open-Loop Alignment 201.3.2.2 Closed-Loop Alignment 201.3.3 Advanced Technologies for Self-alignment Strategies 211.3.3.1 Self-alignment of Fiber to PIC Through an Silicon Optical Bench Using Flip-Chip 221.3.3.2 Self-alignment-assisted Microlenses Assembly 221.3.3.3 Self-alignment of Polymer Waveguides 221.3.3.4 Self-alignment of Optical Plug 231.3.4 Laser/PIC Coupling 231.4 Electronic/Photonic Convergence 251.4.1 Flip-chip Interconnects 261.4.1.1 Standard Die-to-die interconnects 261.4.1.2 Advanced Interconnects for Future Needs 271.4.2 Intra-connections (Through Silicon Vias and Through Glass Vias) 291.4.2.1 TSV Last Process 291.4.2.2 TSV Middle Process 301.4.2.3 Through Glass Via (TGV) 311.4.3 Fan-out Wafer-level Packaging (FOWLP) 311.4.4 Interposers Integration Approach 321.4.4.1 Interposers for Electronic Integrated Circuits (CMOS) 331.4.4.2 Photonic Interposer and Photonic Systems on Chip 341.5 Toward an Ecosystem in Test and Assembly of PICs 361.5.1 Design Rules for Packaging and Test 361.5.1.1 3D Packaging 381.5.1.2 Design Rules for Testing 391.5.2 Advanced Techniques for Wafer-level Test 391.5.3 Recent Achievements and Future Aspects in Assembly Machines 401.6 Conclusion 45Acknowledgments 46References 462 The Last Mile Technology of Silicon Photonics Toward Productions and Emerging Applications 53Bo Li, Shawn Yohanes Siew, Feng Gao, Shawn Wu Xie, Qiang Li, Chao Li, Xianshu Luo, Guo-Qiang Lo, and Junfeng Song2.1 Introduction 532.2 Fiber-to-Chip Assembly 552.3 Hybrid Integration of Light Source 592.4 Electronic and Photonic Co-Packaging 632.5 Outlook 652.5.1 Silicon Photonics Emerging Applications 652.5.2 Opportunities and Challenges 68References 703 Integrated Nonlinear Photonics and Emerging Applications 75Yang Yue, Wenpu Geng, Yuxi Fang, and Yingning Wang3.1 Introduction 753.2 Supercontinuum 773.2.1 Applications 773.2.2 History of SCG in Integrated Waveguides 793.2.3 Representative Works 833.3 Optical Frequency Comb 903.3.1 Microresonator-Based OFC 913.3.2 SC-Based OFC 993.3.3 EO-Based OFC 993.3.4 MLL-Based OFC 993.3.5 Applications 1013.4 Nonlinear Wave Mixing 1023.4.1 Introduction 1023.4.2 Nonlinear Optical Signal Processing in Integrated Waveguides 1053.4.3 Representative Works 1083.5 Conclusion and Perspectives 116References 1174 Excitation, Generation, Positioning, and Modulation for Quantum Light Sources Integrated on Chip 135Cuo Wu, Cuiping Ma, and Zhiming Wang4.1 Introduction 1354.2 Excitation and Orientation of Quantum Emitters 1364.3 Chip-Scale Integration Based on Quantum Emitters 1414.3.1 Solution-Based Colloidal and Self-Assembled Quantum Dots 1414.3.2 Strain-Induced Emitter Sites of Two-Dimensional Materials 1444.3.3 Color Centers in Nanodiamond 1484.4 Deterministically Positioning of Quantum Emitter 1544.5 Quantum Light Interaction with Metasurface for Modulation 1564.6 Conclusion 159References 1605 Quantum Light Sources in Two-Dimensional Materials 167Yanan Wang and Philip X.-L. Feng5.1 Introduction 1675.2 Theory of Quantum Light Sources 1685.2.1 Photon Statistics 1685.2.1.1 Thermal Light 1695.2.1.2 Coherent Light 1705.2.1.3 Squeezed Light 1705.2.2 Characteristics of Quantum Light Sources 1725.2.2.1 Wavelength 1725.2.2.2 Lifetime, Emission Rate, and Brightness 1725.2.2.3 Emission Linewidth 1735.2.2.4 Zero-Phonon Line (ZPL) and Debye–Waller Factor 1735.2.2.5 Photon Polarization and Dipole Orientation 1735.2.2.6 Optically Addressable Spin State 1745.2.2.7 Indistinguishability 1745.3 Quantum Light Sources in 2D Materials 1755.3.1 Localized Excitons in Transition Metal Dichalcogenides 1765.3.2 Defect Centers in Hexagonal Boron Nitride 1795.3.3 Graphene Quantum Dots 1835.3.4 Quantum Light-Emitting Diodes 1865.4 Integration with On-Chip Components 1895.4.1 Theory of SPE-Cavity Coupling 1905.4.1.1 Strong Coupling Regime 1905.4.1.2 Weak Coupling Regime 1915.4.2 Integration with Dielectric Waveguides and Cavities 1915.4.2.1 Transferring 2D SPEs onto Predefined Structures 1925.4.2.2 Transferring or Fabricating Photonic Structures on 2D Materials 1945.4.2.3 Monolithic Integration 1955.4.3 Integration with Plasmonic Waveguides and Cavities 1975.5 Integration with Off-Chip Components 1995.5.1 Flip-chip Integration 1995.5.2 Integration with Optic Fibers 2005.6 Summary and Outlook 202Acknowledgments 203References 2046 Inverse Design for Integrated Photonics Using Deep Neural Network 209Keisuke Kojima, Toshiaki Koike-Akino, Yingheng Tang, and Ye Wang6.1 Introduction 2096.2 Deep Neural Network (DNN) Models 2106.2.1 Forward Modeling 2116.2.2 Inverse Modeling 2126.2.3 Generative Modeling 2126.3 Deep Learning for Forward Modeling to Predict Optical Response 2126.4 Deep Learning for Inverse Modeling to Construct Device Topology 2176.5 Deep Learning for Generative Modeling to Produce Device Topology Candidates 2206.6 Physics-informed Neural Networks 2256.7 Nanophotonic Power Splitter Design Using Generative Modeling 2276.7.1 Device Structure 2286.7.2 Device Simulation Procedure 2296.7.3 Network Architecture 2306.7.4 Network Training Procedure 2316.7.5 Device Generation Performance 2326.7.6 Hyperparameters 2346.7.7 Adjoint Method vs. Deep Learning 2346.8 Deep Learning Techniques 2356.8.1 Convolutional Neural Networks 2356.8.2 Transfer Learning and Fine Tuning 2356.8.3 AutoML: Meta Learning, Learning to Learn, Network Architecture Search 2366.9 Conclusion 237References 2377 Deep Learning Driven Data Processing, Modeling, and Inverse Design for Nanophotonics 245Peter R. Wiecha, Nicholas J. Dinsdale, and Otto L. Muskens7.1 Introduction 2457.2 Artificial Neural Networks and Deep Learning 2457.2.1 Artificial Neurons and Neural Networks 2467.2.2 Training of Artificial Neural Networks 2477.3 Ultrafast Physics Predictions 2487.3.1 Specialized Physics Predictors: Fully Connected vs. Convolutional ANNs 2497.3.2 Generalized Nanophotonics Predictor Network 2527.4 Photonics Inverse Design 2557.4.1 Predictor Network as a Surrogate Model for Optimization 2567.4.1.1 Example: Polarization Conversion Maximization 2577.4.1.2 Example: Maximize Magnetic Near-Field 2587.4.2 Direct Inverse Design Networks 2597.4.3 Optimizing Inverse Design Performance 2607.4.3.1 Optimizing the Network Layout 2627.4.3.2 Quality of the Initial Dataset 2627.4.3.3 Iterative Training 2647.4.3.4 Postprocessing 2657.5 Advanced Data Processing for Photonics Applications 2657.5.1 Optical Data Storage below the Diffraction Limit 2657.5.2 Speckle Reconstruction for Real-time Hyperspectral Imaging 2677.6 Conclusion and Outlook 269References 2708 Optical Waveguide of Lithium Niobate Nanophotonics 277Yarub Al-Douri8.1 Introduction 2778.2 Photonics Lithium Niobate 2788.3 Nanophotonic Lithium Niobate-Based Optical Waveguide 2868.4 Optical Studies of Nanophotonic Lithium Niobate-Based Optical Waveguide 2878.5 Nanophotonic LiNbO 3 Under Stirrer Time Effect 2958.6 Nanophotonic Studies of LiNbO 3 Under Stirrer Time Effect 2978.7 Conclusions 304References 3059 Active, Tunable, and Reconfigurable Nanophotonics 313Trevon 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 Rho9.1 Introduction 3139.2 Liquid Crystal-Integrated Tunable Devices 3149.2.1 Devices that Modulate Polarization 3149.2.2 Devices that Modulate Effective Refractive Index 3169.3 Optically Tunable Devices 3189.3.1 Devices that Are Dependent on the Direction of Incident Light 3189.3.2 Devices that Depend on Wavelength 3199.3.3 Devices that Depend on Polarization (Spin) 3219.3.4 Orbital Angular Momentum-dependent Devices 3239.4 Phase Change Materials-Based Reconfigurable Devices 3249.4.1 Switchable Absorbers 3249.4.2 Thermochromic Smart Windows 3279.5 Mechanically Tunable Photonic Devices 3299.5.1 Tunable Devices that Use Micro-electro-mechanical Systems 3299.5.2 Photonic Devices that Are Tuned Using Strain 3319.6 Tunable Photonic Devices with Material Engineering 3359.6.1 Bandgap Engineering for Tunable Solid-state Devices 3359.6.2 Biomaterials for Tunable Biophotonic Devices 3399.7 Electrically Tunable Photonic Devices 341Acknowledgments 346References 346Index 359