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

Hyojung Kim

doi:10.1039/d5ma00844a

January 1, 2026Materials AdvancesOpen Access

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

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Key Points

The research aims to explore the potential of lead-free halide perovskite memristors for energy-efficient in-memory computing.
Evaluated the performance of halide perovskite materials in memristor applications.
Analyzed ionic migration behavior to assess energy efficiency.
Investigated integration with thin-film electronics for edge computing.
Halide perovskites exhibited rapid ionic migration, enhancing memory access speed.
Demonstrated significant reductions in energy consumption compared to traditional memory solutions.
Successful integration with thin-film electronics indicates practical application for edge computing.

Implication

Edge computing needs faster, low-energy memory solutions; this study reveals the potential of halide perovskite memristors.

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...

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Cite This Study

Hyojung Kim (2026) studied this question.

synapsesocial.com/papers/69cf5ced5a333a821460a81b https://doi.org/10.1039/d5ma00844a

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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