The artificial intelligence era has witnessed a surge of demand in detection and recognition of biometric information,with applications from financial services to information security.However,the physical separation o...The artificial intelligence era has witnessed a surge of demand in detection and recognition of biometric information,with applications from financial services to information security.However,the physical separation of sensing,memory,and computational units in traditional biometric systems introduces severe decision latency and operational power consumption.Herein,an in-sensor reservoir computing(RC)system based on MoTe_(2)/BaTiO_(3)optical synapses is proposed to detect and recognize the faces and fingerprints information.In optical operation mode,the device exhibits low energy consumption of 41.2 pJ,long retention time of 3×10^(4)s,high endurance of 10^(4)switching cycles,and multifunctional sensing-memory-computing visual simulations.The light intensity-dependent optical sensing and multilevel optical storage properties are exploited to achieve sunburned eye simulation and image memory functions.These nonlinear,multi-state,short-term storage,and long-term memory characteristics make MoTe_(2)/BaTiO_(3)optical synapses a suitable reservoir layer and readout layer,with short-term properties to project complicated input features into high-dimensional output features,and long-term properties to be used as a readout layer,thus further building an in-sensor RC system for face and fingerprint recognition.Under the 40%Gaussian noise environment,the system achieves 91.73%recognition accuracy for face and 97.50%for fingerprint images,and experimental verification is carried out,which shows potential in practical applications.These results provide a strategy for constructing a high-performance in-sensor RC system for high-accuracy biometric identification.展开更多
基金supported by the National Key R&D Plan“Nano Frontier”Key Special Project(Grant No.2021YFA1200502)Cultivation Projects of National Major R&D Project(Grant No.92164109)+13 种基金the National Natural Science Foundation of China(Grant Nos.61874158,62004056,and 62104058)the Special Project of Strategic Leading Science and Technology of Chinese Academy of Sciences(Grant No.XDB44000000-7)Key R&D Plan Projects in Hebei Province(Grant No.22311101D)Hebei Basic Research Special Key Project(Grant No.F2021201045)the Support Program for the Top Young Talents of Hebei Province(Grant No.70280011807)the Supporting Plan for 100 Excellent Innovative Talents in Colleges and Universities of Hebei Province(Grant No.SLRC2019018)the Interdisciplinary Research Program of Natural Science of Hebei University(No.DXK202101)the Institute of Life Sciences and Green Development(No.521100311)the Natural Science Foundation of Hebei Province(Nos.F2022201054 and F2021201022)the Outstanding Young Scientific Research and Innovation Team of Hebei University(Grant No.605020521001)the Special Support Funds for National High Level Talents(Grant No.041500120001)the Advanced Talents Incubation Program of the Hebei University(Grant Nos.521000981426,521100221071,and 521000981363)the Science and Technology Project of Hebei Education Department(Grant Nos.QN2020178 and QN2021026)Postgraduate's Innovation Fund Project of Hebei Province(CXZZBS2024004).
文摘The artificial intelligence era has witnessed a surge of demand in detection and recognition of biometric information,with applications from financial services to information security.However,the physical separation of sensing,memory,and computational units in traditional biometric systems introduces severe decision latency and operational power consumption.Herein,an in-sensor reservoir computing(RC)system based on MoTe_(2)/BaTiO_(3)optical synapses is proposed to detect and recognize the faces and fingerprints information.In optical operation mode,the device exhibits low energy consumption of 41.2 pJ,long retention time of 3×10^(4)s,high endurance of 10^(4)switching cycles,and multifunctional sensing-memory-computing visual simulations.The light intensity-dependent optical sensing and multilevel optical storage properties are exploited to achieve sunburned eye simulation and image memory functions.These nonlinear,multi-state,short-term storage,and long-term memory characteristics make MoTe_(2)/BaTiO_(3)optical synapses a suitable reservoir layer and readout layer,with short-term properties to project complicated input features into high-dimensional output features,and long-term properties to be used as a readout layer,thus further building an in-sensor RC system for face and fingerprint recognition.Under the 40%Gaussian noise environment,the system achieves 91.73%recognition accuracy for face and 97.50%for fingerprint images,and experimental verification is carried out,which shows potential in practical applications.These results provide a strategy for constructing a high-performance in-sensor RC system for high-accuracy biometric identification.
