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A Semi-floating Body Memory Featuring Brain-like Behaviors.

Chengjie Tang1, Xiaolong Ma1, Kang Wang1

  • 1State Key Laboratory of Integrated Chips and Systems, Fudan University, 220 Handan Road, 200433 Shanghai, China.

ACS Applied Materials & Interfaces
|December 29, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel semi-floating body memory (SFBM) device that mimics human brain memory functions. The SFBM demonstrates extended retention times and revisitation behavior, paving the way for advanced brain-inspired computing.

Keywords:
brain-inspired computingcompute-in-memory macrofloating body effectmemorymetal/graphene/silicon heterojunction

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Area of Science:

  • Electronic Devices
  • Neuroscience
  • Materials Science

Background:

  • Mimicking human brain behaviors in electronic devices is a long-standing goal in computing.
  • Existing neuromorphic systems require further innovation for advanced functionalities.

Purpose of the Study:

  • To present a novel semi-floating body memory (SFBM) device with human brain-like memory behaviors.
  • To demonstrate the potential of SFBM for brain-inspired computing and content-addressable memory applications.

Main Methods:

  • Designed a p-type silicon-on-insulator (SOI) MOSFET with a metal/graphene/silicon heterojunction.
  • Utilized impact ionization (II) at the heterojunction for charge storage in an ungated semi-floating body.
  • Employed back gate control for current quantification.

Main Results:

  • Achieved a retention time of ~1 hour, 300x longer than conventional floating body memory.
  • Extended retention time to over a day through read operations, mimicking memory revisitation.
  • Demonstrated rapid response to write pulses (20 ns) and shock-like behavior.
  • Showcased hormone-like regulation of synaptic weights via back gate control.
  • Experimentally demonstrated a 1T-1S content-addressable memory (CAM) cell for compute-in-memory (CIM).

Conclusions:

  • The SFBM exhibits multiple unique, brain-like memory behaviors.
  • SFBM is a strong candidate for future brain-inspired computing units.
  • The demonstrated CAM cell highlights SFBM's potential for efficient compute-in-memory architectures.