Open Access
REVOLUTIONIZING SILICON PHOTONIC DEVICE DESIGN THROUGH DEEP GENERATIVE MODELS: AN INVERSE APPROACH AND EMERGING TRENDS
4
Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
4
Department of Applied Physics, Stanford University, Stanford, CA, USA
Abstract
The design of silicon photonic devices has traditionally relied on iterative simulation-based methods, which are time-consuming and often limited in exploring the vast design space. Recent advancements in deep generative models have paved the way for a paradigm shift by enabling inverse design approaches that are both efficient and accurate. This study explores how deep generative networks, particularly variational autoencoders (VAEs) and generative adversarial networks (GANs), are revolutionizing the development of silicon photonic structures by mapping desired optical responses directly to geometrical configurations. The paper reviews current methodologies, evaluates their effectiveness in performance optimization, and identifies emerging trends such as physics-informed learning, hybrid generative-discriminative frameworks, and real-time feedback-based design evolution. This convergence of deep learning and photonics promises to accelerate innovation in optical communications, sensing, and integrated photonic circuits.
Keywords
Silicon Photonics,
Deep Generative Models,
Inverse Design,
Photonic Device Optimization
References
Aggarwal A, Mittal M, Battineni G. 2021. Generative adversarial network: an overview of theory and applications. International Journal of Information Management Data Insights 1(1):100004
Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L. 2021. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data 8:53
Anstine DM, Isayev O. 2023. Generative models as an emerging paradigm in the chemical sciences. Journal of the American Chemical Society 145(16):8736-8750
Anzalchi J, Inigo P, Roy B. 2017. Application of photonics in next generation telecommunication satellites payloads.
Arjovsky M, Chintala S, Bottou L. 2017. Wasserstein GAN. Arxiv
Bank D, Koenigstein N, Giryes R. 2023. Autoencoders. In: Rokach L, Maimon O, Smueli E, eds. Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook. Cham: Springer. 353-374
Berente N, Gu B, Recker J, Santhanam R. 2021. Managing artificial intelligence. Mis Quarterly 45(3):1433-1450
Bobokulova MK. 2023. Importance of fiber optic devices in medicine. Multidisciplinary Journal of Science and Technology 3(5):212-216
Bogaerts W, Chrostowski L. 2018. Silicon photonics circuit design: methods, tools and challenges. Laser & Photonics Reviews 12(4):1700237
Borrelli NF. 2017. Microoptics technology: fabrication and applications of lens arrays and devices. Boca Raton: CRC Press.
Butt MA, Khonina SN, Kazanskiy NL. 2021. Recent advances in photonic crystal optical devices: a review. Optics & Laser Technology 142:107265
Capmany J, Pérez D. 2020. Programmable integrated photonics. England: Oxford University Press.
Casellas R, Nadal L, Martinez R, Vilalta R, Muñoz R, Svaluto Moreolo M. 2024. Photonic device programmability in support of autonomous optical networks. Journal of Optical Communications and Networking 16(8):D53–D63
Chandrasekar R, Lapin ZJ, Nichols AS, Braun RM, Fountain III AW. 2019. Photonic integrated circuits for department of defense-relevant chemical and biological sensing applications: state-of-the-art and future outlooks. Optical Engineering 58(2):020901
Chen J, Hao R, Nasidi I, Zhang H, Wang X, Jin S. 2023. Deep learning-based modelling of complex photonic crystal slow light waveguides. IEEE Journal of Selected Topics in Quantum Electronics 29(6):6101506
Chen M, Lupoiu R, Mao C, Huang D-H, Jiang J, Lalanne P, Fan JA. 2022. High speed simulation and freeform optimization of nanophotonic devices with physics-augmented deep learning. ACS Photonics 9(9):3110-3123
Chen F, Zheng J, Xing C, Sang J, Shen T. 2024. Applications of liquid crystal planer optical elements based on photoalignment technology in display and photonic devices. Displays 82:102632
Chung H, Miller OD. 2020a. High-NA achromatic metalenses by inverse design. Optics Express 28(5):6945-6965
Chung H, Miller OD. 2020b. Tunable metasurface inverse design for 80% switching efficiencies and 144 angular deflection. ACS Photonics 7(8):2236-2243
Deng Y, Ren S, Malof J, Padilla WJ. 2022. Deep inverse photonic design: a tutorial. Photonics and Nanostructures-Fundamentals and Applications 52:101070
Dey A. 2018. Semiconductor metal oxide gas sensors: a review. Materials Science and Engineering: B 229:206-217
Dizaji PS, Habibiyan H, Arabalibeik H. 2022. A miniaturized computational spectrometer with optimum number of nanophotonic filters: deep-learning autoencoding and inverse design-based implementation. Photonics and Nanostructures-Fundamentals and Applications 52:101057
Gao D, Ding W, Nieto-Vesperinas M, Ding X, Rahman M, Zhang T, Lim C, Qiu C-W. 2017. Optical manipulation from the microscale to the nanoscale: fundamentals, advances and prospects. Light: Science & Applications 6:e17039
Gao L, Li X, Liu D, Wang L, Yu Z. 2019. A bidirectional deep neural network for accurate silicon color design. Advanced Materials 31:1905467
Gostimirovic D, Grinberg Y, Xu D-X, Liboiron-Ladouceur O. 2023. Improving fabrication fidelity of integrated nanophotonic devices using deep learning. ACS Photonics 10(6):1953-1961
Goutzoulis AP. 2021. Design and fabrication of acousto-optic devices. Boca Raton: CRC Press.
Guo X, Xu X, Li Y, Huang W. 2022. Extendable neural network and flexible extendable neural network in nanophotonics. Optics Communications 508:127671
Head S, Keshavarz Hedayati M. 2022. Inverse design of distributed bragg reflectors using deep learning. Applied Sciences 12(10):4877
Ho J, Jain A, Abbeel P. 2020. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems ArXiv
Hong Y, Nicholls DP. 2022. Data-driven design of thin-film optical systems using deep active learning. Optics Express 30:22901-22910
Hwang H-S, Lee M, Seok J. 2022. Deep reinforcement learning with a critic-value-based branch tree for the inverse design of two-dimensional optical devices. Applied Soft Computing 127:109386
Jackson PC. 2019. Introduction to artificial intelligence. Mineola: Courier Dover Publications.
Javaid M, Haleem A, Rab S, Singh RP, Suman R. 2021. Sensors for daily life: a review. Sensors International 2:100121
Jiang J, Chen M, Fan JA. 2021. Deep neural networks for the evaluation and design of photonic devices. Nature Reviews Materials 6:679-700
Jiang J, Fan JA. 2020. Multiobjective and categorical global optimization of photonic structures based on ResNet generative neural networks. Nanophotonics 10(1):361-369
Jiang A, Yoshie O. 2022. A reinforcement learning method for optical thin-film design. IEICE Transactions on Electronics E105.C(2):95-101
Kang C, Park C, Lee M, Kang J, Jang MS, Chung H. 2024a. Large-scale photonic inverse design: computational challenges and breakthroughs. Nanophotonics 13(20):3765-3792
Kang C, Seo D, Boriskina SV, Chung H. 2024b. Adjoint method in machine learning: a pathway to efficient inverse design of photonic devices. Materials & Design 239:112737
Kerbl B, Kopanas G, Leimkühler T, Drettakis G. 2023. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics 42:4
Khaireh-Walieh A, Langevin D, Bennet P, Teytaud O, Moreau A, Wiecha PR. 2023. A newcomer’s guide to deep learning for inverse design in nano-photonics. Nanophotonics 12(24):4387-4414
Kiarashinejad Y, Abdollahramezani S, Adibi A. 2020. Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures. NPJ Computational Materials 6:12
Kim DY, Choi S, Cho H, Sun J-Y. 2019. Electroactive soft photonic devices for the synesthetic perception of color and sound. Advanced Materials 31:1804080
Kim W, Kim S, Lee M, Seok J. 2022. Inverse design of nanophotonic devices using generative adversarial networks. Engineering Applications of Artificial Intelligence 115:105259
Kim I, Martins RJ, Jang J, Badloe T, Khadir S, Jung H-Y, Kim H, Kim J, Genevet P, Rho J. 2021. Nanophotonics for light detection and ranging technology. Nature Nanotechnology 16:508-524
Kojima K, Tahersima MH, Koike-Akino T, Jha DK, Tang Y, Wang Y, Parsons K. 2021. Deep neural networks for inverse design of nanophotonic devices. Journal of Lightwave Technology 39(4):1010-1019
Li Y, Deng M, Liu Z, Peng P, Chen Y, Fang Z. 2022. Inverse design of unidirectional transmission nanostructures based on unsupervised machine learning. Advanced Optical Materials 10:2200127
Liu Z, Lin C-H, Hyun B-R, Sher C-W, Lv Z, Luo B, Jiang F, Wu T, Ho C-H, Kuo H-C+1 more. 2020. Micro-light-emitting diodes with quantum dots in display technology. Light: Science & Applications 9:83
Liu G-X, Liu J-F, Zhou W-J, Li L-Y, You C-L, Qiu C-W, Wu L. 2023. Inverse design in quantum nanophotonics: combining local-density-of-states and deep learning. Nanophotonics 12(11):1943-1955
Liu G-X, Liu J-F, Zhou W-J, Wu L. 2022. Inverse design local-density-of-states via deep learning in quantum nanophotonics.
Liu Z, Zhu D, Raju L, Cai W. 2021. Tackling photonic inverse design with machine learning. Advanced Science 8:2002923
Ma Z, Li Y. 2020. Parameter extraction and inverse design of semiconductor lasers based on the deep learning and particle swarm optimization method. Optics Express 28:21971-21981
Ma L, Wang S, Li Y, Wang G, Duan X. 2022. The accelerated design of the nanoantenna arrays by deep learning. Nanotechnology 33:485204
Mao S, Cheng L, Zhao C, Khan FN, Li Q, Fu H. 2021. Inverse design for silicon photonics: from iterative optimization algorithms to deep neural networks. Applied Sciences 11(9):3822
Mentzer M. 2017. Applied optics fundamentals and device applications: nano, MOEMS, and biotechnology. Boca Raton: CRC Press.
Mildenhall B, Srinivasan PP, Tancik M, Barron JT, Ramamoorthi R, Ng R. 2021. NeRF: representing scenes as neural radiance fields for view synthesis. Communications of the ACM 65(1):99-106
Ming Qing Y, Feng Ma H, Wei Wu L, Jun Cui T. 2020. Manipulating the light-matter interaction in a topological photonic crystal heterostructure. Optics Express 28:34904-34915
Molesky S, Lin Z, Piggott AY, Jin W, Vucković J, Rodriguez AW. 2018. Inverse design in nanophotonics. Nature Photonics 12:659-670
Moore EA, Smart LE. 2020. Optical properties of solids. In: Solid State Chemistry. Boca Raton: CRC Press. 283-314
Mroz AM, Posligua V, Tarzia A, Wolpert EH, Jelfs KE. 2022. Into the unknown: how computation can help explore uncharted material space. Journal of the American Chemical Society 144(41):18730-18743
Okamoto K. 2021. Fundamentals of optical waveguides. Amsterdam, Netherlands: Elsevier.
Ou Q-D, Li Y-Q, Tang J-X. 2016. Light manipulation in organic photovoltaics. Advanced Science 3:1600123
Pan Z, Pan X. 2023. Deep learning and adjoint method accelerated inverse design in photonics: a review. Photonics 10(7):852
Qiao J, Zhou G, Zhou Y, Zhang Q, Xia Z. 2019. Divalent europium-doped near-infrared-emitting phosphor for light-emitting diodes. Nature Communications 10:5267
Ren Y, Zhang L, Wang W, Wang X, Lei Y, Xue Y, Sun X, Zhang W. 2021. Genetic-algorithm-based deep neural networks for highly efficient photonic device design. Photonics Research 9:B247–B252
Sajedian I, Badloe T, Rho J. 2019. Optimisation of colour generation from dielectric nanostructures using reinforcement learning. Optics Express 27:5874-5883
Saleh BE, Teich MC. 2019. Fundamentals of photonics. New York: John Wiley & Sons.
Salehi A, Fu X, Shin D-H, So F. 2019. Recent advances in OLED optical design. Advanced Functional Materials 29:1808803
Sanchez-Lengeling B, Aspuru-Guzik A. 2018. Inverse molecular design using machine learning: generative models for matter engineering. Science 361(6400):360-365
Schubert EF. 2018. Light-emitting diodes (2018). Troy: E. Fred Schubert.
Shi R, Huang J, Li S, Niu L, Yang J. 2022. Forward prediction and inverse design of nanophotonic devices based on capsule network. IEEE Photonics Journal 14(4):1-8
So S, Mun J, Rho J. 2019. Simultaneous inverse design of materials and structures via deep learning: demonstration of dipole resonance engineering using core–shell nanoparticles. ACS Applied Materials & Interfaces 11(27):24264-24268
Song Y, Wang D, Qin J, Li J, Ye H, Zhang Z, Chen X, Zhang M, Boucouvalas AC. 2021. Physical information-embedded deep learning for forward prediction and inverse design of nanophotonic devices. Journal of Lightwave Technology 39(20):6498-6508
Song Y, Wang D, Ye H, Qin J, Zhang M. 2020. Wavelength controllable forward prediction and inverse design of nanophotonic devices using deep learning.
Sujecki S. 2018. Photonics modelling and design. Boca Raton: CRC Press.
Sutton RS, Barto AG. 2018. Reinforcement learning: an introduction. Cambridge: MIT Press.
Tang Y, Kojima K, Koike-Akino T, Wang Y, Wu P, Xie Y, Tahersima MH, Jha DK, Parsons K, Qi M. 2020. Generative deep learning model for inverse design of integrated nanophotonic devices. Laser & Photonics Reviews 14:2000287
Taud H, Mas J-F. 2018. Multilayer perceptron (MLP) In: Geomatic Approaches for Modeling Land Change Scenarios. Cham: Springer. 451-455
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł., Polosukhin I. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30:5998-6008
Verganti R, Vendraminelli L, Iansiti M. 2020. Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management 37:212-227
Wang X, Cui Y, Li T, Lei M, Li J, Wei Z. 2019. Recent advances in the functional 2d photonic and optoelectronic devices. Advanced Optical Materials 7:1801274
Wiecha PR, Arbouet A, Girard C, Muskens OL. 2021. Deep learning in nano-photonics: inverse design and beyond. Photonics Research 9:B182-B200
Zhang C, Lu Y. 2021. Study on artificial intelligence: the state of the art and future prospects. Journal of Industrial Information Integration 23:100224
Zhao C, Wang J, Mao S, Liu X, Kin W, Chan V, Fu H. 2023. End-to-end optimization for a compact optical neural network based on nanostructured 2 × 2 optical processors. IEEE Photonics Journal 15(5):1-8
Zhao Z, You J, Zhang J, Tang Y. 2022. Data-enhanced deep greedy optimization algorithm for the on-demand inverse design of tmdc-cavity heterojunctions. Nanomaterials 12(17):2976
Zheng L, Liu Z, Xin S, Chen Q, Ming J, Wu L, Xu J, Xu P, Liu K, Seeram R+1 more. 2024. Flexible electrolyte-gated transistor based on InZnSnO nanowires for self-adaptive applications. Applied Materials Today 41:102424
Zhu L, Li Y, Yang Z, Zong D, Liu Y. 2023. An on-demand inverse design method for nanophotonic devices based on generative model and hybrid optimization algorithm. Plasmonics 19:1279-1290
Zhu J, Liu X, Shi Q, He T, Sun Z, Guo X, Liu W, Sulaiman OB, Dong B, Lee C. 2019. Development trends and perspectives of future sensors and MEMS/NEMS. Micromachines 11(1):7
Similar Articles
- Dr. Mateo Alvarez, Integrative Perspectives On Identity, Authentication, And Privacy: From RFID Security Protocols To Facial Biometric Representations , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 01 (2026): Volume 03 Issue 01
- Dr. Alejandro Moreno, An Explainable, Context-Aware Zero-Trust Identity Architecture for Continuous Authentication in Hybrid Device Ecosystems , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 11 (2025): Volume 02 Issue 11
- Dr. Aris Thorne, Generating Dual-Identity Face Impersonations with Generative Adversarial Networks: An Adversarial Attack Methodology , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Severov Arseni Vasilievich, Artyom V. Smirnov, Architecting Real-Time Risk Stratification in the Insurance Sector: A Deep Convolutional and Recurrent Neural Network Framework for Dynamic Predictive Modeling , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Bagus Candra, Minh Thu Nguyen, A Comprehensive Evaluation Of Shekar: An Open-Source Python Framework For State-Of-The-Art Persian Natural Language Processing And Computational Linguistics , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Dr. Leila K. Moreno, Integrated Real-Time Fraud Detection and Response: A Streaming Analytics Framework for Financial Transaction Security , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 11 (2025): Volume 02 Issue 11
- Dr. Lukas Reinhardt, Next-Generation Security Operations Centers: A Holistic Framework Integrating Artificial Intelligence, Federated Learning, and Sustainable Green Infrastructure for Proactive Threat Mitigation , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 09 (2025): Volume 02 Issue 09
- Dr. Kenji Yamamoto, Prof. Lijuan Wang, LEVERAGING DEEP LEARNING IN SURVIVAL ANALYSIS FOR ENHANCED TIME-TO-EVENT PREDICTION , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 05 (2025): Volume 02 Issue 05
- Angelo soriano, Sheila Ann Mercado, The Convergence of AI And UVM: Advanced Methodologies for the Verification of Complex Low-Power Semiconductor Architectures , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 11 (2025): Volume 02 Issue 11
- Dr. Anya Sharma, Leveraging Geospatial Context and Population Attributes for Hyper-Personalized E-Commerce Recommendations , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 09 (2025): Volume 02 Issue 09
1-10 of 35
Next
You may also start an advanced similarity search for this article.