Abstract
? 2022 Elsevier B.V.Artificial photonic synapse devices (PSDs) hold great promise for the realization of next-generation artificial vision systems and processing units through a synergistic combination of brain-inspired neuromorphic computing and high levels of parallelism. Here, we demonstrate artificial PSDs based on solution-processed In-Sn-Zn-O (indium-tin-zinc oxide, ITZO) thin films capable of mimicking various neuromorphic functions. In particular, a transistor structure was adopted for PSDs to enable a facile control of the photo-response characteristics by gate biasing. With optimized gate bias condition, enhanced electrical conductance modulation was possible which can improve the energy efficiency of PSDs. In addition, we investigated the dependency of photo-response characteristics on light pulse waveforms to find out the correlation between various pulse parameters and the photo-current generation. Based on these findings, we demonstrated the emulation of associative learning which is one of the important cognitive functions of the brain. Moreover, to verify the translation of optically derived synaptic behaviors of ITZO PSDs into artificial neuromorphic computing, pattern recognition of handwritten digit patterns was demonstrated showing an accuracy up to 90.3%.
NSTL
Elsevier