Dr. Dajun Lin Profile

Dr. Dajun Lin

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Proceedings Article | 20 December 2022 Presentation

Bilayer diffractive neural networks integrated on quartz substrate for handwritten digital recognition

Yibo Dong, Xinyuan Fang, Dajun Lin, Haitao Luan, Xi Chen, Qiming Zhang, Min Gu

Proceedings Volume 12318, 123180E (2022) https://doi.org/10.1117/12.2641974

KEYWORDS: Quartz, Neural networks, Semiconducting wafers, Holography, Digital holography, Diffraction, Wafer-level optics, Semiconductor manufacturing, Optical logic, Nervous system

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The rapid development of artificial intelligence has stimulated the interest of the novel designs of photonic neural networks (PNNs) because of the high speed, low energy consumption and parallelism nature of the light. Based on optical holographic technology, a kind of three-dimensional PNNs, diffractive neural networks (DNNs), have demonstrated their superb performance in parallel two-dimensional data processing. DNNs are composed of multi-layer cascaded holographic plates. Relying on the diffraction of the incident light, each pixel in every layer can be connected with multiple pixels in the next layer to mimic the architecture of the biological nervous system. Important applications, such as image recognition, optical logic operation, and image reconstruction, have been realized on DNNs with high operation efficiency. However, in most of the reported works, the layers of DNNs are spatially separated with a large size of centimeter-scale, which greatly limits the on-chip integration of DNNs. In this work, we reported a green-light bilayer integrated DNNs. The two layers of the DNNs were integrated on the double sides of a quartz wafer respectively by lithography followed by dry etching. Based on the theory of diffraction, the DNNs were trained with a size of millimeter-scale. When the DNNs work, the incident optical signal first passes through the 1st layer of the DNNs, then diffracts inside the quartz wafer, and finally emitted out from the 2nd layer of the DNNs on the backside. Handwritten digital recognition of 0~1 (89 % accuracy) or 0~9 (65% accuracy) was successfully realized. The high stability of quartz provides the basis for the long-term reliable operation of DNNs. The manufacturing of the DNNs is compatible with the mature semiconductor manufacturing technology, which provides a feasible route for the macro fabrication of DNNs.

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