Properties that are similar to the memory and learning functions in biological systems have been observed and reported in the experimental studies of memristors fabricated by different materials. These properties incl...Properties that are similar to the memory and learning functions in biological systems have been observed and reported in the experimental studies of memristors fabricated by different materials. These properties include the forgetting effect, the transition from short-term memory（STM） to long-term memory（LTM）, learning-experience behavior, etc. The mathematical model of this kind of memristor would be very important for its theoretical analysis and application design.In our analysis of the existing memristor model with these properties, we find that some behaviors of the model are inconsistent with the reported experimental observations. A phenomenological memristor model is proposed for this kind of memristor. The model design is based on the forgetting effect and STM-to-LTM transition since these behaviors are two typical properties of these memristors. Further analyses of this model show that this model can also be used directly or modified to describe other experimentally observed behaviors. Simulations show that the proposed model can give a better description of the reported memory and learning behaviors of this kind of memristor than the existing model.展开更多
Artificial synapses are electronic devices that simulate important functions of biological synapses,and therefore are the basic components of artificial neural morphological networks for brain-like computing.One of th...Artificial synapses are electronic devices that simulate important functions of biological synapses,and therefore are the basic components of artificial neural morphological networks for brain-like computing.One of the most important objectives for developing artificial synapses is to simulate the characteristics of biological synapses as much as possible,especially their self-adaptive ability to external stimuli.Here,we have successfully developed an artificial synapse with multiple synaptic functions and highly adaptive characteristics based on a simple SrTiO_(3)/Nb:SrTiO_(3)heterojunction type memristor.Diverse functions of synaptic learning,such as short-term/long-term plasticity(STP/LTP),transition from STP to LTP,learning–forgetting–relearning behaviors,associative learning and dynamic filtering,are all bio-realistically implemented in a single device.The remarkable synaptic performance is attributed to the fascinating inherent dynamics of oxygen vacancy drift and diffusion,which give rise to the coexistence of volatile-and nonvolatile-type resistive switching.This work reports a multi-functional synaptic emulator with advanced computing capability based on a simple heterostructure,showing great application potential for a compact and low-power neuromorphic computing system.展开更多
文摘Properties that are similar to the memory and learning functions in biological systems have been observed and reported in the experimental studies of memristors fabricated by different materials. These properties include the forgetting effect, the transition from short-term memory（STM） to long-term memory（LTM）, learning-experience behavior, etc. The mathematical model of this kind of memristor would be very important for its theoretical analysis and application design.In our analysis of the existing memristor model with these properties, we find that some behaviors of the model are inconsistent with the reported experimental observations. A phenomenological memristor model is proposed for this kind of memristor. The model design is based on the forgetting effect and STM-to-LTM transition since these behaviors are two typical properties of these memristors. Further analyses of this model show that this model can also be used directly or modified to describe other experimentally observed behaviors. Simulations show that the proposed model can give a better description of the reported memory and learning behaviors of this kind of memristor than the existing model.
基金the National Key Research&Development Program of China(No.2021YFB3601504)the National Natural Science Foundation of China(Nos.52072218,12222414,12074416)+2 种基金the Natural Science Foundation of Shandong province(Nos.ZR2022YQ43 and ZR2020ZD28)Heilongjiang Provincial Natural Resources Foundation Joint Guide Project(No.LH2020E098)Peixin Fund of Qilu University of Technology(Shandong Academy of Sciences)(No.2023PY093).
文摘Artificial synapses are electronic devices that simulate important functions of biological synapses,and therefore are the basic components of artificial neural morphological networks for brain-like computing.One of the most important objectives for developing artificial synapses is to simulate the characteristics of biological synapses as much as possible,especially their self-adaptive ability to external stimuli.Here,we have successfully developed an artificial synapse with multiple synaptic functions and highly adaptive characteristics based on a simple SrTiO_(3)/Nb:SrTiO_(3)heterojunction type memristor.Diverse functions of synaptic learning,such as short-term/long-term plasticity(STP/LTP),transition from STP to LTP,learning–forgetting–relearning behaviors,associative learning and dynamic filtering,are all bio-realistically implemented in a single device.The remarkable synaptic performance is attributed to the fascinating inherent dynamics of oxygen vacancy drift and diffusion,which give rise to the coexistence of volatile-and nonvolatile-type resistive switching.This work reports a multi-functional synaptic emulator with advanced computing capability based on a simple heterostructure,showing great application potential for a compact and low-power neuromorphic computing system.
