From light sensing to adaptive learning: hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing

Abstract

Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior, dynamic responses, and energy efficiency characteristics. Although charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity, replicating the key functionality of neurons—integrating diverse presynaptic inputs to fire electrical impulses—has remained challenging. In this study, we developed reconfigurable metal-oxide-semiconductor capacitors (MOSCaps) based on hafnium diselenide (HfSe₂). The proposed devices exhibit (1) optoelectronic synaptic features and perform separate stimulus-associated learning, indicating considerable adaptive neuron emulation, (2) dual light-enabled charge-trapping and memcapacitive behavior within the same MOSCap device, whose threshold voltage and capacitance vary based on the light intensity across the visible spectrum, (3) memcapacitor volatility tuning based on the biasing conditions, enabling the transition from volatile light sensing to non-volatile optical data retention. The reconfigurability and multifunctionality of MOSCap were used to integrate the device into a leaky integrate-and-fire neuron model within a spiking neural network to dynamically adjust firing patterns based on light stimuli and detect exoplanets through variations in light intensity.