Publication date: 21st July 2025
Analog memristors without conductive filaments are promising candidates for neuromorphic and in-memory computing due to their superior multilevel switching capabilities, reproducibility, and low energy consumption. Among these, halide perovskite-based memristors—typically fabricated in metal–insulator–metal (MIM) architectures—leverage interfacial valence change mechanisms (VCM) or self-doping to modulate the Schottky barrier and achieve analog resistive switching. However, most reported devices exhibit highly non-linear current–voltage (I–V) characteristics, with a very narrow ohmic region, which limits their effectiveness in applications such as vector-matrix multiplication (VMM), where linear device response is essential for accurate and efficient computation.
Moreover, these devices often suffer from capacitive current spikes during transient measurements at low read voltages, which interfere with state detection. Although increasing the read voltage (>1 V) can suppress these transients, it risks disturbing the memristor’s programmed state—an undesirable tradeoff in practical applications.
In this work, we present a strategy to significantly broaden the linear/ohmic region in halide perovskite analog memristors through electrochemical doping induced by bias-driven metal ion migration from the top electrode. This results in an order-of-magnitude expansion of the ohmic window without compromising the analog switching characteristics. Structural and compositional analyses using scanning transmission electron microscopy with energy-dispersive X-ray spectroscopy (STEM-EDX) confirm the presence of migrated metal within the perovskite layer, while Kelvin probe force microscopy (KPFM) evidences the resulting local doping effect.
The engineered device exhibits stable and reproducible analog switching across 32 distinct conductance levels, each with retention exceeding 1000 seconds—key metrics for reliable in-memory computing. By combining filament-free operation, enhanced ohmicity, and robust multilevel retention, our approach addresses a fundamental limitation in halide perovskite memristors and paves the way for their integration into efficient and scalable neuromorphic architectures.
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