Publication date: 15th April 2025
In the era of artificial intelligence (AI), which is deeply integrated into our daily lives, big data processing technologies and AI memory systems are gaining significant attention as key solutions to overcome the bottleneck limitations of the von Neumann architecture. Among various next-generation memory devices, halide perovskite (HP)-based memristors are emerging as promising candidates for neuromorphic computing systems due to their unique ionic, electrical, and material properties. These include high ionic mobility, low power consumption, ease of fabrication, flexible integration compatibility, and non-volatile memory behavior. However, there remains a lack of comprehensive dynamic physical analysis and detailed understanding of the operating mechanisms and characteristics of HP-based memristors and neuromorphic devices, which are crucial for realizing high-performance, accurate future applications.
In this work, I present an in-depth exploration of the various characteristics, operating principles, and dynamic physical models of HP-based memristors and neuromorphic devices. First, I classify the different switching mechanisms based on the arrangement and movement of cations (such as electroactive metals like Ag, Cu, etc.), anions (halide ions), and vacancies within the HP layer. I will then explain how these switching types correspond to specific memory and neuromorphic device applications. Additionally, I introduce physical models, including impedance spectroscopy and time response analysis, to investigate the drift-diffusion dynamics of resistive switching properties driven by ion migration in the HP layer. These dynamic behaviors are essential not only for memory devices but also for mimicking biological synapses, enabling the integration of HP-based memristors into neuromorphic computing architectures.
Ultimately, this presentation offers a comprehensive overview of the mechanisms and electrical actuation in memory and neuromorphic systems, providing valuable insights from multiple perspectives for future high-performance applications.
This work was funded by the European Research Council (ERC) via Horizon Europe Advanced Grant, grant agreement nº 101097688 (“PeroSpiker”)
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