指纹(计算)
计算机科学
指纹识别
紫外线
记忆电阻器
人工智能
模式识别(心理学)
材料科学
纳米技术
光电子学
工程类
电气工程
作者
Zhongfang Zhang,Xiaolong Zhao,Xumeng Zhang,Xiaohu Hou,Xiaolan Ma,Shuangzhu Tang,Ying Zhang,Guangwei Xu,Qi Liu,Shibing Long
标识
DOI:10.1038/s41467-022-34230-8
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI