Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization

• URL: http://arxiv.org/abs/2506.20056v2
• Date: Sat, 26 Jul 2025 17:37:47 GMT
• Title: Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization
• Authors: Yuheng Chen, Alexander Montes McNeil, Taehyuk Park, Blake A. Wilson, Vaishnavi Iyer, Michael Bezick, Jae-Ik Choi, Rohan Ojha, Pravin Mahendran, Daksh Kumar Singh, Geetika Chitturi, Peigang Chen, Trang Do, Alexander V. Kildishev, Vladimir M. Shalaev, Michael Moebius, Wenshan Cai, Yongmin Liu, Alexandra Boltasseva,
• Abstract summary: Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications.<n>PDD is an iterative, five-step process that consists of: i.e. deriving device behavior from design parameters, ii. simulating device performance, iv. fabricating the optimal device, and v. measuring device performance.<n>PDD suffers from large optimization landscapes, uncertainties in structural or optical characterization, and difficulties in implementing robust fabrication processes.<n>In this review, we present a comprehensive perspective on these methods to enable machine-learning-assisted PDD
• Score: 80.82828320306464
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies [66.83950068218033]
  Scaling Laws demonstrate that scaling model parameters and training data enhances learning performance.<n>Despite its potential to improve performance, the integration of scaling laws into deep reinforcement learning has not been fully realized.<n>This review addresses this gap by systematically analyzing scaling strategies in three dimensions: data, network, and training budget.
  arXiv  Detail & Related papers  (2025-08-05T08:03:12Z)
• ESM: A Framework for Building Effective Surrogate Models for Hardware-Aware Neural Architecture Search [4.9276746621153285]
  Hardware-aware Neural Architecture Search (NAS) is one of the most promising techniques for designing efficient Deep Neural Networks (DNNs) for resource-constrained devices.<n>We study different types of surrogate models and highlight their strengths and weaknesses.<n>We present a holistic framework that enables reliable dataset generation and efficient model generation, considering the overall costs of different stages of the model generation pipeline.
  arXiv  Detail & Related papers  (2025-08-02T22:06:39Z)
• MAPS: Multi-Fidelity AI-Augmented Photonic Simulation and Inverse Design Infrastructure [18.220589086200025]
  Inverse design has emerged as a transformative approach for photonic device optimization.<n>We introduce MAPS, a multi-fidelity AI-augmented photonic simulation and inverse design infrastructure.
  arXiv  Detail & Related papers  (2025-03-02T22:30:18Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving [52.808273563372126]
  This paper proposes a novel hierarchical BEV perception paradigm, aiming to provide a library of fundamental perception modules and user-friendly graphical interface. We conduct the Pretrain-Finetune strategy to effectively utilize large scale public datasets and streamline development processes. We also present a Multi-Module Learning (MML) approach, enhancing performance through synergistic and iterative training of multiple models.
  arXiv  Detail & Related papers  (2024-07-17T11:17:20Z)
• A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures [9.215792818120903]
  This paper critically reviews influential tools and design methodologies for Deep Learning accelerators.<n>It covers hardware-software co-design, automated synthesis, domain-specific compilers, design space exploration, modeling, and simulation.
  arXiv  Detail & Related papers  (2023-11-29T17:10:16Z)
• Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation [17.164961143132473]
  We introduce a learning framework that demonstrates the efficacy of aligning the sampling trajectory of diffusion models with the optimization trajectory derived from traditional physics-based methods. Our method allows for generating feasible and high-performance designs in as few as two steps without the need for expensive preprocessing, external surrogate models, or additional labeled data. Our results demonstrate that TA outperforms state-of-the-art deep generative models on in-distribution configurations and halves the inference computational cost.
  arXiv  Detail & Related papers  (2023-05-29T09:16:07Z)
• Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions [0.0]
  We review advances in AI-driven materials-by-design and their applications to energetic materials. We evaluate methods in the literature in terms of their capacity to learn from a small/limited number of data. We suggest a few promising future research directions for EM materials-by-design, such as meta-learning, active learning, Bayesian learning, and semi-/weakly-supervised learning.
  arXiv  Detail & Related papers  (2022-11-15T14:41:11Z)
• Hybrid Supervised and Reinforcement Learning for the Design and Optimization of Nanophotonic Structures [8.677532138573984]
  This paper presents a hybrid supervised and reinforcement learning approach to the inverse design of nanophotonic structures. We show this approach can reduce training data dependence, improve the generalizability of model predictions, and shorten exploratory training times by orders of magnitude.
  arXiv  Detail & Related papers  (2022-09-08T22:43:40Z)
• Predictive modeling approaches in laser-based material processing [59.04160452043105]
  This study aims to automate and forecast the effect of laser processing on material structures. The focus is centred on the performance of representative statistical and machine learning algorithms. Results can set the basis for a systematic methodology towards reducing material design, testing and production cost.
  arXiv  Detail & Related papers  (2020-06-13T17:28:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.