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

2025-09-26

AI Summary

Biological learning relies on temporally dynamic synaptic plasticity and neuromodulator-mediated global regulation, yet conventional memristors struggle to simultaneously achieve spatiotemporal tunability and ultralow power consumption. To address this, we propose optical irradiation as a neuromodulator-mimicking global control signal and implement photoelectric-cooperative dynamic synaptic functionality using nanoscale strontium titanate (SrTiO₃, STO) memristors. We observe a deterministic square-root dependence of conductance change on light intensity, and demonstrate that the photoinduced conductance decay time constant (1–10 s) is electrically tunable—faithfully emulating biologically relevant temporal plasticity. The device achieves an ultralow single-pulse energy consumption of 1 pJ, with minimal variability and high operational reliability. This work presents the first solid-state memristor enabling optically controlled, electrically tunable, and ultralow-power biomimetic dynamic learning, establishing a new paradigm for energy-efficient neuromorphic computing hardware.

Technology Category

• Machine Learning: Bio-inspired Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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 - who influence many synapses at once depending on their dynamic, internal state. In this study, using optical radiation as a global neuromodulatory signal, we investigate nanoscale SrTiO3 (STO) memristors that can act as solid-state synapses. Via diverse sets of measurements, we demonstrate that the memristor's photoresponse depends on the electrical conductance state, following a well-defined square root relation. Additionally, we show that the conductance decays after photoexcitation with time constants in the range of 1 - 10 s and that this effect can be reliably controlled using an electrical bias. These properties in combination with our 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.

Problem

• Developing dynamic memristors as solid-state synapses for biorealistic computing
• Investigating conductance-dependent photoresponse using optical neuromodulatory signals
• Controlling photoexcitation decay with electrical bias for energy-efficient learning

Innovation

• Optical radiation serves as global neuromodulatory signal
• SrTiO3 memristors act as solid-state synapses
• Photoresponse depends on conductance via square root relation