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

By Zhongfang Zhang, Xiaolong Zhao, Xumeng Zhang, Xiaohu Hou, Xiaolan Ma, Shuangzhu Tang, Ying Zhang, Guangwei Xu, Qi Liu, S. Long. Nature Communications, 2022

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The separation of sensor, memory, and processor in a recognition system deteriorates the latency of decision-making and increases the overall computing power. Here, Zhang et al. develop a photoelectronic reservoir computing system, consisting of DUV photo-synapses and a memristor array, to detect and recognize the latent fingerprint with in-sensor and parallel in-memory computing. 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 GaO_ x (a-GaO_ x ) photo-synapses with an enhanced persistent photoconductivity (PPC) effect. The PPC effect, which induces nonlinearly tunable conductivity, renders the a-GaO_ x 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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4555465de28e6896378097e9801f3b1133a140c4