On-chip Learning Neural Networks with Ag/CeO2/ITO Memristors
The existing memristor neural network has the problems of slow learning rate,low accuracy and complex circuit.In order to achieve standard and efficient on-chip learning,an on-chip learning neural network based on Ag/CeO2/ITO memristor is designed.A transistor and two Ag/CeO2/ITO memristors are used as synaptic neurons,and the synaptic structure has a larger weight range.The threshold characteristics of Ag/CeO2/ITO memristor simplify the steps and circuit structure of non-destructive reading.Each layer of the neural network can update the weights in parallel in two clock period.This method avoids the delay,power consumption and error caused by data transmission.Finally,the proposed memristor neural network is simulated and applied to the recognition of character images and iris flowers.The recognition accuracy can reach more than 95％,and the difference of memristor devices has little impact on the accuracy,which proves the effectiveness and robustness of the proposed memristor neural network.
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