Conductance‐Dependent Photoresponse in a Dynamic SrTiO 3 Memristor for Biorealistic Computing

ABSTRACT Modern computers perform pre‐defined operations using static memory components, whereas biological systems learn through inherently dynamic, time‐dependent processes in synapses and neurons. The biological learning process also relies on global signals—neuromodulators—that influence many synapses at once, depending on their dynamic, internal state. In this study, using optical radiation as a global neuromodulatory signal, nanoscale SrTiO 3 (STO) memristors that can act as solid‐state synapses are investigated. It is observed that the memristor's photo‐conductance exhibits a long‐term decay process after photoexcitation (10s of seconds) with an activation energy of 0.33 eV. Based on density functional theory calculations, this long‐term photoresponse is attributed to the generation and migration of oxygen vacancies at the Pt‐SrTiO 3 interface. Additionally, the photo‐conductance decay can be accurately controlled through an electrical bias signal and the magnitude of the memristor's photoresponse depends on its electrical conductance state, following a well‐defined square root relation. These properties, in combination with the device's low power operation (< 1pJ per optical pulse) and small measurement variability, may pave the way for space‐ and energy‐efficient implementations of complex biological learning processes in electro‐optical hardware.