Publication date: 21st July 2025
With the rise of artificial intelligence (AI) and its deep integration into modern life, efficient big data processing and bio-inspired technology for neuromorphic computing become crucial for overcoming the inherent bottleneck of the von Neumann architecture. Among next-generation memories, halide perovskites (HP)-based memristors have emerged as strong candidates for multi-functional and neuromorphic computing electronic devices due to their ionic-electronic migration properties, low power consumption, facile fabrication, non-volatile switching behavior, etc. However, a clear understanding of their operating mechanisms and physical behavior analysis remains limited, hindering their optimized design for multifunctional bio-inspired applications.
This presentation outlines the fundamental electrical characteristics, material design strategies, switching mechanisms, and physical dynamics of HP-based memristors, with a focus on their applicability in multifunctional and bio-inspired systems. Compositional engineering of HP materials has been shown to significantly influence switching behavior by modulating switching mechanisms in terms of ionic and defect migration pathways. The classification of switching modes and types is discussed in relation to the formation and modulation of conductive pathways involving mobile ions within the HP layer. These mechanisms are examined in the context of their relevance to reliable memory and neuromorphic functionality. To further elucidate device dynamics, various physical analysis techniques are introduced, including impedance spectroscopy (IS) and time-domain response modeling, which provide insight into the ionic drift-diffusion dynamics and relaxation behavior of the system. These techniques enable evaluation of essential properties such as nonlinearity, memory retention, and synaptic-like plasticity. Equivalent circuit models derived from frequency-domain analysis are also considered, offering a foundation for understanding complex behaviors such as hysteresis, rectification, and adaptive conductance tuning in HP-based memristors.
Altogether, these perspectives support the advancement of halide perovskite memristors as promising building blocks for energy-efficient, scalable, multi-functional bio-inspired computing systems.
This work was funded by the European Research Council (ERC) via Horizon Europe Advanced Grant, grant agreement nº 101097688 (“PeroSpiker”)
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