A manufacture-friendly design framework for optical neural networks

Yang Zhao; Ye Tian; Shengping Liu; Wei Wang; Qiang Li; Junbo Feng; Jin Guo; Jianzhong Han

doi:10.1117/12.2580470

5 November 2020 A manufacture-friendly design framework for optical neural networks

Yang Zhao, Ye Tian, Shengping Liu, Wei Wang, Qiang Li, Junbo Feng, Jin Guo, Jianzhong Han

Proceedings Volume 11565, AOPC 2020: Display Technology; Photonic MEMS, THz MEMS, and Metamaterials; and AI in Optics and Photonics; 1156514 (2020) https://doi.org/10.1117/12.2580470
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China

Abstract

We introduced a Python-based design framework for manufacture-friendly optical ANN. It provides the cross-level interoperability between the photonic circuit chip layout and the neural networks infrastructure to enable the optical ANN with better tolerance to the device-by-device or chip-by-chip deviation. It allows a wide range of abstract on levels to describe the behavior of optical ANN: from the lowest-level functionality of manipulating the properties and arrangement of individual phase shifters on a photonic circuit chip, to the highest-level features of designing optical ANN via PyTorchlike development-library as well as its optimization with the well-established machine learning algorithms such as backpropagation. On all the levels, the physical design of the photonic circuit chip can be integrated and synchronized with the construction of the neural networks accounting the influences of the fabrication-deviation with the assistance of IPKISS, a Python-based tool for photonic circuit design. As a demonstration, we use our framework to design the LeNet-5 networks, which can be executed on the photonic circuit chip with non-uniformed grating coupling efficiency. Our LeNet-5 networks achieves the precision around 97.5% for MNIST task.

Citation Download Citation

Yang Zhao, Ye Tian, Shengping Liu, Wei Wang, Qiang Li, Junbo Feng, Jin Guo, and Jianzhong Han "A manufacture-friendly design framework for optical neural networks", Proc. SPIE 11565, AOPC 2020: Display Technology; Photonic MEMS, THz MEMS, and Metamaterials; and AI in Optics and Photonics, 1156514 (5 November 2020); https://doi.org/10.1117/12.2580470

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KEYWORDS

Optics manufacturing

Neural networks

Integrated optics

Photonic integrated circuits

Optical design

Design for manufacturability

Modulators

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