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Nanostructured perovskites for nonvolatile memory devices.

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Perovskite materials offer advanced electronic device capabilities. This review highlights how perovskite dimensionality impacts emerging nonvolatile memory devices (eNVMs) for storage and neuromorphic computing applications.

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Device Engineering

Background:

  • Perovskite materials are crucial for advanced electronic devices due to their low cost, facile synthesis, and excellent optoelectronic properties.
  • Emerging nonvolatile memory devices (eNVMs) based on perovskites are revolutionizing storage and computation paradigms.
  • The influence of perovskite material dimensionality on device performance is underexplored.

Purpose of the Study:

  • To review recent advances in perovskite-based eNVMs, focusing on material dimensionality.
  • To explore the potential of perovskite structures in storage and neuromorphic computing.
  • To provide insights for designing high-performance perovskite electronics.

Main Methods:

  • Review of recent literature on perovskite-based memristors and field-effect transistors.
  • Analysis of material preparation methods and device structures.
  • Evaluation of working mechanisms and unique features of perovskite eNVMs.

Main Results:

  • Perovskite dimensionality significantly influences the performance of eNVMs.
  • Successful implementation of perovskite electronics in hardware-based neural networks, in-sensor computing, and logic operations.
  • Demonstration of perovskite-based physical unclonable functions and true random number generators.

Conclusions:

  • Dimensionality engineering is key for optimizing perovskite-based electronics.
  • Perovskite eNVMs show great promise for next-generation AI, information security, and computing.
  • This review serves as a benchmark for future perovskite electronic device development.