AI Enabled Lead-free Halide Perovskite Memristor Crossbars Array Energy Efficient In Memory Computing

Abstract

Recent edge computing is progressively requiring memories that provide rapid access, low energy consumption, and smooth integration with adaptable thin-film electronics. Halide perovskites meet these criteria as ionic migration within their flexible lattice results in strong, field-tunable conductance that remains effective without the need for charge storage. The focus of research has thus transitioned to benign, lead-free compositions that inhibit parasitic ion drift, extend data retention, and enhance moisture tolerance. This review looks into the latest developments in halide perovskite crossbar arrays. Uniform polycrystalline layers created via solution or vacuum methods demonstrate wide resistance ranges, remarkably low leakage, and consistent multilevel conductance throughout prolonged cycling. Selector-integrated cells effectively reduce sneak paths, ensuring signal integrity throughout closely linked planar meshes and vertically arranged networks. Additionally, their inherent rectifying properties allow for completely passive nodes that are ideal for analog inference. The density, switching speed, and adaptive behavior already achieved confirm that lead-free halide perovskite memristors represent a promising platform for energy-efficient data storage and on-device intelligence.