Machine-learning-assisted photonic device development: a multiscale approach from theory to characterization

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Publicat a:Nanophotonics vol. 14, no. 23 (2025), p. 3761
Autor principal: Chen, Yuheng
Altres autors: Alexander Montes McNeil, Park, Taehyuk, Wilson, Blake A, Iyer, Vaishnavi, Bezick, Michael, Jae-Ik Choi, Ojha, Rohan, Mahendran, Pravin, Singh, Daksh Kumar, Chitturi, Geetika, Chen, Peigang, Do, Trang, Kildishev, Alexander V, Shalaev, Vladimir M, Moebius, Michael, Cai, Wenshan, Liu, Yongmin, Boltasseva, Alexandra
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Walter de Gruyter GmbH
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100 1 |a Chen, Yuheng  |u Elmore Family School of Electrical and Computer Engineering, Birck Nanotechnology Center, and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA; Quantum Science Center, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA 
245 1 |a Machine-learning-assisted photonic device development: a multiscale approach from theory to characterization 
260 |b Walter de Gruyter GmbH  |c 2025 
513 |a Journal Article 
520 3 |a Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications, including telecommunications, imaging, sensing, and quantum information processing. PDD is an iterative, five-step process that consists of: (i) deriving device behavior from design parameters, (ii) simulating device performance, (iii) finding the optimal candidate designs from simulations, (iv) fabricating the optimal device, and (v) measuring device performance. Classically, all these steps involve Bayesian optimization, material science, control theory, and direct physics-driven numerical methods. However, many of these techniques are computationally intractable, monetarily costly, or difficult to implement at scale. In addition, PDD suffers from large optimization landscapes, uncertainties in structural or optical characterization, and difficulties in implementing robust fabrication processes. However, the advent of machine learning over the past decade has provided novel, data-driven strategies for tackling these challenges, including surrogate estimators for speeding up computations, generative modeling for noisy measurement modeling and data augmentation, reinforcement learning for fabrication, and active learning for experimental physical discovery. In this review, we present a comprehensive perspective on these methods to enable machine-learning-assisted PDD (ML-PDD) for efficient design optimization with powerful generative models, fast simulation and characterization modeling under noisy measurements, and reinforcement learning for fabrication. This review will provide researchers from diverse backgrounds with valuable insights into this emerging topic, fostering interdisciplinary efforts to accelerate the development of complex photonic devices and systems. 
653 |a Machine learning 
653 |a Design optimization 
653 |a Data augmentation 
653 |a Quantum phenomena 
653 |a Control theory 
653 |a Data processing 
653 |a Multiscale analysis 
653 |a Measuring instruments 
653 |a Modelling 
653 |a Photonics 
653 |a Structural analysis 
653 |a Design parameters 
653 |a Numerical methods 
653 |a Optical properties 
700 1 |a Alexander Montes McNeil  |u Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA; Draper Scholar, The Charles Stark Draper Laboratory, Inc., Cambridge, MA 02139, USA 
700 1 |a Park, Taehyuk  |u School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA 
700 1 |a Wilson, Blake A  |u Elmore Family School of Electrical and Computer Engineering, Birck Nanotechnology Center, and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA; Quantum Science Center, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA 
700 1 |a Iyer, Vaishnavi  |u Elmore Family School of Electrical and Computer Engineering, Birck Nanotechnology Center, and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA; Quantum Science Center, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA 
700 1 |a Bezick, Michael  |u Elmore Family School of Electrical and Computer Engineering, Birck Nanotechnology Center, and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA 
700 1 |a Jae-Ik Choi  |u Elmore Family School of Electrical and Computer Engineering, Birck Nanotechnology Center, and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA; Quantum Science Center, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA 
700 1 |a Ojha, Rohan  |u Elmore Family School of Electrical and Computer Engineering, Birck Nanotechnology Center, and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA 
700 1 |a Mahendran, Pravin  |u Elmore Family School of Electrical and Computer Engineering, Birck Nanotechnology Center, and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA 
700 1 |a Singh, Daksh Kumar  |u Elmore Family School of Electrical and Computer Engineering, Birck Nanotechnology Center, and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA; Quantum Science Center, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA 
700 1 |a Chitturi, Geetika  |u Elmore Family School of Electrical and Computer Engineering, Birck Nanotechnology Center, and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA 
700 1 |a Chen, Peigang  |u Elmore Family School of Electrical and Computer Engineering, Birck Nanotechnology Center, and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA; Quantum Science Center, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA 
700 1 |a Do, Trang  |u Elmore Family School of Electrical and Computer Engineering, Birck Nanotechnology Center, and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA 
700 1 |a Kildishev, Alexander V  |u Elmore Family School of Electrical and Computer Engineering, Birck Nanotechnology Center, and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA 
700 1 |a Shalaev, Vladimir M  |u Elmore Family School of Electrical and Computer Engineering, Birck Nanotechnology Center, and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA; Quantum Science Center, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA 
700 1 |a Moebius, Michael  |u The Charles Stark Draper Laboratory, Inc., Cambridge, MA 02139, USA 
700 1 |a Cai, Wenshan  |u School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA 
700 1 |a Liu, Yongmin  |u Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA; Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA 
700 1 |a Boltasseva, Alexandra  |u Elmore Family School of Electrical and Computer Engineering, Birck Nanotechnology Center, and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA; Quantum Science Center, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA 
773 0 |t Nanophotonics  |g vol. 14, no. 23 (2025), p. 3761 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3271915129/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3271915129/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch