Title

Photonic synapses with ultralow energy consumption for artificial visual perception and brain storage

Document Type

Article

Publication Date

9-1-2022

Keywords

memory, MoS synaptic transistors 2, ultralow power consumption, visual perception

Abstract

The human visual system, dependent on retinal cells, can be regarded as a complex combination of optical system and nervous system. Artificial retinal system could mimic the sensing and processing function of human eyes. Optically stimulated synaptic devices could serve as the building blocks for artificial retinas and subsequent information transmission system to brain. Herein, photonic synaptic transistors based on polycrystalline MoS2, which could simulate human visual perception and brain storage, are presented. Moreover, the photodetection range from visible light to near-infrared light of MoS2 multilayer could extend human eyes’ vision limitation to near-infrared light. Additionally, the photonic synaptic transistor shows an ultrafast speed within 5 μs and ultralow power consumption under optical stimuli about 40 aJ, sever-al orders of magnitude lower than biological synapses (50 ms and 10 fJ). Furthermore, the backgate control could act as emotional modulation of the artificial brain to enhance or suppress memory function, i.e. the intensity of photoresponse. The proposed carrier trapping/detrapping as the main working mechanism is presented for the device. In addition, synaptic functionalities including short synaptic plasticity, long synaptic plasticity and paired-pulse facilitation could be successfully simulated based on the prepared device. Furthermore, the large difference between short synaptic plasticity and long synaptic plasticity reveals the better image pre-processing function of the prepared photonic synapses. The classical Pavlovian conditioning associated with the associative learning is successfully implemented as well. Therefore, the efficient and rich functionalities demonstrate the potential of the MoS2 synaptic device that integrates sensing-memory-preprocessing capabilities for realizing artificial neural networks with different emotions that mimic human retina and brain.

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