Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip

Elena Goi; Xi Chen; Qiming Zhang; Benjamin P. Cumming; Steffen Schoenhardt; Haitao Luan; Min Gu

doi:10.1038/s41377-021-00483-z

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Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip

Articles

- Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip
- Light: Science & Applications Vol. 10, Issue 3, Pages: 409-419(2021)
- Affiliations：
  
  1.Centre for Artificial-Intelligence Nanophotonics, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
  2.Laboratory for Artificial-Intelligence Nanophotonics, School of Science, RMIT University, Melbourne, VIC 3001, Australia
- Author bio：
  
  Min Gu (gumin@usst.edu.cn)
- Funds：
- DOI：10.1038/s41377-021-00483-z
  CLC：
- Published：2021，
  
  Published Online：03 March 2021，
  
  Received：22 September 2020，
  
  Revised：17 January 2021，
  
  Accepted：29 January 2021
- Accepted：
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Goi, E. et al. Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip. Light: Science & Applications, 10, 409-419 (2021).
DOI：

Goi, E. et al. Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip. Light: Science & Applications, 10, 409-419 (2021). DOI： 10.1038/s41377-021-00483-z.

摘要

Abstract

Optical machine learning has emerged as an important research area that

by leveraging the advantages inherent to optical signals

such as parallelism and high speed

paves the way for a future where optical hardware can process data at the speed of light. In this work

we present such optical devices for data processing in the form of single-layer nanoscale holographic perceptrons trained to perform optical inference tasks. We experimentally show the functionality of these passive optical devices in the example of decryptors trained to perform optical inference of single or whole classes of keys through symmetric and asymmetric decryption. The decryptors

designed for operation in the near-infrared region

are nanoprinted on complementary metal-oxide-semiconductor chips by galvo-dithered two-pho

ton nanolithography with axial nanostepping of 10 nm

achieving a neuron density of

500 million neurons per square centimetre. This power-efficient commixture of machine learning and on-chip integration may have a transformative impact on optical decryption

sensing

medical diagnostics

and computing

关键词

Keywords

references

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