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Current infrared recognition systems are built on the von Neumann architecture and complementary metal\u2010oxide\u2010semiconductor image sensors, which encounter limited spectral adaptability with serious data transmission delay and high power consumption. Here, a compact in\u2010sensor reservoir computing (RC) system is demonstrated using van der Waals\n                    <jats:italic>\u03b1<\/jats:italic>\n                    \u2010In\n                    <jats:sub>2<\/jats:sub>\n                    Se\n                    <jats:sub>3<\/jats:sub>\n                    optoelectronic device to implement the face recognition task in near\u2010infrared band. Due to the light\u2010induced temporal ferroelectric polarization field in\n                    <jats:italic>\u03b1<\/jats:italic>\n                    \u2010In\n                    <jats:sub>2<\/jats:sub>\n                    Se\n                    <jats:sub>3<\/jats:sub>\n                    , the nonlinear photoresponse and short\u2010term memory characteristics are achieved in the device with low optical energy consumption of \u224815\u2009pJ. Additionally, the device exhibits diverse synaptic plasticity with excellent separation of the optical information based on the dual\u2010feature strategy. An in\u2010sensor RC system is constructed through employing the\n                    <jats:italic>\u03b1<\/jats:italic>\n                    \u2010In\n                    <jats:sub>2<\/jats:sub>\n                    Se\n                    <jats:sub>3<\/jats:sub>\n                    optoelectronic synapse as the physical node, which achieves 99.5% recognition accuracy with fast training speed. The robustness of the system is further verified by the preservation of accuracy at 87.1% under high noise level of 20%. This work envisions ferroelectric optoelectronic synapse as potential building blocks for in\u2010sensor RC system, paving the way for achieving high\u2010accuracy recognition system in extended spectral range.\n                  <\/jats:p>","DOI":"10.1002\/aisy.202401073","type":"journal-article","created":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T08:53:53Z","timestamp":1749718433000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["In\u2010Sensor Reservoir Computing Using Ferroelectric Optoelectronic Synapse for Near\u2010Infrared Face Recognition"],"prefix":"10.1002","volume":"7","author":[{"given":"Wenyu","family":"Songlu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Integrated Chips and Systems Frontier Institute of Chip and System Fudan University  Shanghai 200433 China"},{"name":"School of Microelectronics Fudan University  Shanghai 200437 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Shanghai 200240 China"}]},{"given":"Weihui","family":"Sang","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics and Department of Materials Science Fudan University  Shanghai 200433 China"}]},{"given":"Yang","family":"Gan","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics and Department of Materials Science Fudan University  Shanghai 200433 China"}]},{"given":"Shijia","family":"Tian","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics and Department of Materials Science Fudan University  Shanghai 200433 China"}]},{"given":"Yuan","family":"Yu","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Research Base of Intelligent 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