Abstract
With the advent of the generation of artificial intelligence (AI) based on big data-processing technologies, next-generation memristor and memristive neuromorphic devices have been actively studied with great interest to overcome the von Neumann bottleneck limits. Among various candidates, halide perovskites (HPs) have been in the spotlight as potential candidates for these devices due to their unique switching characteristics with low energy consumption and flexible integration compatibility across various sources for scalability. We outline the characteristics and operating principles of HP-based memristors and their neuromorphic devices. We explain filamentary- and interface-type switching according to the type of conducting pathway occurring inside the active HP layer and the operating mechanisms depending on the species that make up this conducting pathway. We summarize the types and mechanisms of current changes beneficial for neuromorphic device applications and finally organize various suggested analysis tools and physical models to enable experimental determination of switching mechanisms from various perspectives.
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