In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array

Zhongfang Zhang, Xiaolong Zhao (), Xumeng Zhang (), Xiaohu Hou, Xiaolan Ma, Shuangzhu Tang, Ying Zhang, Guangwei Xu, Qi Liu and Shibing Long ()
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Zhongfang Zhang: University of Science and Technology of China
Xiaolong Zhao: University of Science and Technology of China
Xumeng Zhang: Fudan University
Xiaohu Hou: University of Science and Technology of China
Xiaolan Ma: University of Science and Technology of China
Shuangzhu Tang: Fudan University
Ying Zhang: University of Science and Technology of China
Guangwei Xu: University of Science and Technology of China
Qi Liu: Fudan University
Shibing Long: University of Science and Technology of China

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract Detection and recognition of latent fingerprints play crucial roles in identification and security. However, the separation of sensor, memory, and processor in conventional ex-situ fingerprint recognition system seriously deteriorates the latency of decision-making and inevitably increases the overall computing power. In this work, a photoelectronic reservoir computing (RC) system, consisting of DUV photo-synapses and nonvolatile memristor array, is developed to detect and recognize the latent fingerprint with in-sensor and parallel in-memory computing. Through the Ga-rich design, we achieve amorphous GaOx (a-GaOx) photo-synapses with an enhanced persistent photoconductivity (PPC) effect. The PPC effect, which induces nonlinearly tunable conductivity, renders the a-GaOx photo-synapses an ideal deep ultraviolet (DUV) photoelectronic reservoir, thus mapping the complex input vector into a dimensionality-reduced output vector. Connecting the reservoirs and a memristor array, we further construct an in-sensor RC system for latent fingerprint identification. The system maintains over 90% recognition accuracy for latent fingerprint within 15% stochastic noise level via the proposed dual-feature strategy. This work provides a subversive prototype system of DUV in-sensor RC for highly efficient recognition of latent fingerprints.

Date: 2022
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DOI: 10.1038/s41467-022-34230-8

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