1. Compact eternal diffractive neural network chip for extreme environments
- Author
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Yibo Dong, Dajun Lin, Long Chen, Baoli Li, Xi Chen, Qiming Zhang, Haitao Luan, Xinyuan Fang, and Min Gu
- Subjects
Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
Abstract Artificial intelligence applications in extreme environments place high demands on hardware robustness, power consumption, and speed. Recently, diffractive neural networks have demonstrated superb advantages in high-throughput light-speed reasoning. However, the robustness and lifetime of existing diffractive neural networks cannot be guaranteed, severely limiting their compactness and long-term inference accuracy. Here, we have developed a millimeter-scale and robust bilayer-integrated diffractive neural network chip with virtually unlimited lifetime for optical inference. The two diffractive layers with binary phase modulation were engraved on both sides of a quartz wafer. Optical inference of handwritten digital recognition was demonstrated. The results showed that the chip achieved 82% recognition accuracy for ten types of digits. Moreover, the chip demonstrated high-performance stability at high temperatures. The room-temperature lifetime was estimated to be 1.84×1023 trillion years. Our chip satisfies the requirements for diffractive neural network hardware with high robustness, making it suitable for use in extreme environments.
- Published
- 2024
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