Flexible Electronic Synapses for Face Recognition Application with Multimodulated Conductance States.

Abstract

An artificial synaptic device with a continuous weight modulation behavior is fundamental to the hardware implementation of the bioinspired neuromorphic systems. Recent reported synaptic devices have a less number of conductance states, which is not beneficial for the continuous modulation of weights in neuromorphic computing. Preparing a device with as many conductance states as possible is of great significance to the development of brain-inspired neuromorphic computing. Here, we present a two-terminal flexible organic synaptic device with ultra-multimodulated conductance states, realizing a face recognition functionality with a strong error-tolerant nature for the first time. The device shows an excellent long-term potentiation or long-term depression behavior and reliability after 1000 folded destructive tests. There are 600 continuous ultra-multimodulated conductance states, which can be used to realize the great face recognition capability. The recognition rates were 95.2% and above 90% for the initial and 15% noise pixel images, respectively. The strong error-tolerant nature indicates a potential application of a flexible organic artificial synaptic device with ultra-multimodulated conductance states in the large-scale neuromorphic systems.