Conductance-Dependent Photoresponse in a Dynamic SrTiO₃ Memristor for Biorealistic Computing

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 SrTiO3 (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-SrTiO3 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.