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Molecularly Engineered Wing-Shaped Azobenzene Memristors for Logic-in-Memory and Edge Visual Intelligence.

Yanze Liu1,2, Tao Han3, Jiahui Ding1,2

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We developed novel organic molecular resistive memory devices using azobenzene-based molecules. These devices exhibit tunable memory behaviors and demonstrate potential for advanced neuromorphic computing and in-memory logic operations.

Keywords:
azobenzene derivativescharge transferlogic gatesneuromorphic computingorganic resistive random access memory

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Area of Science:

  • Organic electronics
  • Materials science
  • Nanotechnology

Background:

  • The von Neumann bottleneck limits computing efficiency.
  • Organic molecular resistive memory offers a potential solution.
  • Azobenzene-based molecules are explored for memristive applications.

Purpose of the Study:

  • To design and investigate novel azobenzene-based small molecules for memristive applications.
  • To explore the relationship between molecular structure and memory characteristics.
  • To demonstrate the potential of these materials for in-memory computing and neuromorphic architectures.

Main Methods:

  • Synthesis of four symmetric azobenzene-based small molecules with diverse terminal substituents.
  • Fabrication and characterization of resistive memory devices.
  • Investigation of nonvolatile memory behaviors (WORM, bipolar resistive memory).
  • Mechanistic studies involving charge-transfer and conformational changes.
  • Evaluation of synaptic functions and logic-in-memory operations.
  • Application demonstration in CNN-based image edge detection.

Main Results:

  • Tunable nonvolatile memory behaviors (ternary/binary WORM, bipolar) achieved by modifying terminal groups.
  • Devices exhibit high ON/OFF ratios, low operating voltages, and excellent stability.
  • Charge-transfer-induced conformational changes dictate memory characteristics.
  • Demonstrated continuous conductance tunability and essential synaptic functions (EPSC, PPF, LTP/LTD).
  • Successfully implemented logic gates (OR, AND, XOR, NAND) and adder circuits.
  • Validated applicability for in-memory computing via CNN-based image edge detection.

Conclusions:

  • Azobenzene-based small molecules provide a versatile platform for advanced organic resistive memory.
  • Molecular design enables tunable memory characteristics and synaptic functions.
  • These materials show significant promise for next-generation organic neuromorphic architectures and in-memory computing.