Phase-change material(PCM)is generating widespread interest as a new candidate for artificial synapses in bioinspired computer systems.However,the amorphization process of PCM devices tends to be abrupt,unlike continu...Phase-change material(PCM)is generating widespread interest as a new candidate for artificial synapses in bioinspired computer systems.However,the amorphization process of PCM devices tends to be abrupt,unlike continuous synaptic depression.The relatively large power consumption and poor analog behavior of PCM devices greatly limit their applications.Here,we fabricate a GeTe/Sb2Te3 superlattice-like PCM device which allows a progressive RESET process.Our devices feature low-power consumption operation and potential high-density integration,which can effectively simulate biological synaptic characteristics.The programming energy can be further reduced by properly selecting the resistance range and operating method.The fabricated devices are implemented in both artificial neural networks(ANN)and convolutional neural network(CNN)simulations,demonstrating high accuracy in brain-like pattern recognition.展开更多
基金Project supported by the National Science and Technology Major Project of China(Grant No.2017ZX02301007-002)the National Key R&D Plan of China(Grant No.2017YFB0701701)the National Natural Science Foundation of China(Grant Nos.61774068 and 51772113).The authors acknowledge the support from Hubei Key Laboratory of Advanced Memories&Hubei Engineering Research Center on Microelectronics.
文摘Phase-change material(PCM)is generating widespread interest as a new candidate for artificial synapses in bioinspired computer systems.However,the amorphization process of PCM devices tends to be abrupt,unlike continuous synaptic depression.The relatively large power consumption and poor analog behavior of PCM devices greatly limit their applications.Here,we fabricate a GeTe/Sb2Te3 superlattice-like PCM device which allows a progressive RESET process.Our devices feature low-power consumption operation and potential high-density integration,which can effectively simulate biological synaptic characteristics.The programming energy can be further reduced by properly selecting the resistance range and operating method.The fabricated devices are implemented in both artificial neural networks(ANN)and convolutional neural network(CNN)simulations,demonstrating high accuracy in brain-like pattern recognition.
