An Optogenetics‐Inspired Flexible van der Waals Optoelectronic Synapse and its Application to a Convolutional Neural Network. Issue 40 (22nd August 2021) - Seo, Seunghwan; Lee, Je‐Jun; Lee, Ryong‐Gyu; Kim, Tae Hyung; Park, Sangyong; Jung, Sooyoung; Lee, Hyun‐Kyu; Andreev, Maksim; Lee, Kyeong‐Bae; Jung, Kil‐Su; Oh, Seyong; Lee, Ho‐Jun; et al.
Abstract: Optogenetics refers to a technique that uses light to modulate neuronal activity with a high spatiotemporal resolution, which enables the manipulation of learning and memory functions in the human brain. This strategy of controlling neuronal activity using light can be applied for the development of intelligent systems, including neuromorphic and in‐memory computing systems. Herein, a flexible van der Waals (vdW) optoelectronic synapse is reported, which is a core component of optogenetics‐inspired intelligent systems. This synapse is fabricated on 2D vdW layered rhenium disulfide (ReS2 ) that features an inherent photosensitive memory nature derived from the persistent photoconductivity (PPC) effect, successfully mimicking the dynamics of biological synapses. Based on first‐principles calculations, the PPC effect is identified to originate from sulfur vacancies in ReS2 that have an inherent tendency to form shallow defect states near the conduction band edges and under optical excitation lead to large lattice relaxation. Finally, the feasibility of applying the synapses in optogenetics‐inspired intelligent systems is demonstrated via training and inference tasks for the CIFAR‐10 dataset using a convolutional neural network composed of vdW optoelectronic synapse devices. Abstract : A flexible van der Waals (vdW) optoelectronic synapse fabricated on 2D vdW layered rhenium disulfide, which features an inherent photosensitive memory nature derived from the persistent photoconductivity effect, is proposed. Following in‐depth analysis including density functional theory calculations on rhenium disulfide, its feasibility for hardware neural networks with learning ability is demonstrated using a convolutional neural network composed of vdW optoelectronic synapses.