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All‐optical and fully reconfigurable transmissive diffractive optical neural network (DONN) architectures emerge as high‐throughput and energy‐efficient machine learning (ML) hardware accelerators in broad applications. However, current device and system implementations have limited performance. In this work, a novel transmissive diffractive device architecture, a digitized phase‐change material (PCM) heterostack, which consists of multiple nonvolatile PCM layers with different thicknesses, is demonstrated. Through this architecture, the advantages of PCM electrical and optical properties can be leveraged and challenges associated with multilevel operations in a single PCM layer can be mitigated. Through proof‐of‐concept experiments, the electrical tuning of one PCM layer is demonstrated in a transmissive spatial light modulation device, and thermal analysis guides the design of multilayer devices and DONN systems to avoid thermal cross talk if individual heterostacks are assembled into an array. Further, a heterostack containing three PCM layers is designed based on experimental results to produce a large‐phase modulation range and uniform coverage, and the ML performance of DONN systems with the designed heterostack is evaluated. The developed device architecture is practically feasible and scalable for future energy‐efficient, fast‐reconfigured, and compact transmissive DONN systems.
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Scientific Computing with Diffractive Optical Neural Networks
Diffractive optical neural networks (DONNs) are emerging as high‐throughput and energy‐efficient hardware platforms to perform all‐optical machine learning (ML) in machine vision systems. However, the current demonstrated applications of DONNs are largely image classification tasks, which undermine the prospect of developing and utilizing such hardware for other ML applications. Herein, the deployment of an all‐optical reconfigurable DONNs system for scientific computing is demonstrated numerically and experimentally, including guiding two‐dimensional quantum material synthesis, predicting the properties of two‐dimensional quantum materials and small molecular cancer drugs, predicting the device response of nanopatterned integrated photonic power splitters, and the dynamic stabilization of an inverted pendulum with reinforcement learning. Despite a large variety of input data structures, a universal feature engineering approach is developed to convert categorical input features to images that can be processed in the DONNs system. The results open up new opportunities for employing DONNs systems for a broad range of ML applications.
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- PAR ID:
- 10519487
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Advanced Intelligent Systems
- Volume:
- 5
- Issue:
- 12
- ISSN:
- 2640-4567
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1002/aisy.202300536
