On-receptor computing with classical associative learning in semiconductor oxide memristors

Abstract

The increasing demand for energy-efficient data processing leads to a growing interest in neuromorphic computing that aims to emulate cerebral functions. This approach offers cost-effective and rapid parallel data processing, surpassing the limitations of the conventional von Neumann architecture. Key to this emulation is the development of memristors that mimic biological synapses. Recently, research efforts have focused on the incorporation of nociceptors—sensory neurons capable of detecting external stimuli—into memristors for applications in robotics and artificial intelligence. This integration enables memristors to adapt to various circumstances while remaining cost-effective. A nonfilamentary gradual resistive switching memristor is utilized to implement artificial nociceptor and synaptic behaviors. The fabricated Pt/indium gallium zinc oxide (IGZO)/SnO_x/TiN device exhibits essential properties of biological nociceptors, including threshold response, no-adaptation, relaxation, sensitization, and recovery. Furthermore, the device leverages short-term memory principles to emulate learning behaviors observed in the brain by showcasing “forgetting” paradigms. Additionally, control of the input spikes yields different synaptic plasticity responses, thus emulating the key functions of our synapse. Computational simulations demonstrate the device's ability to perform both computing and sensing tasks effectively, thus enabling on-receptor computing with associative learning capabilities.