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A reconfigurable photosensitive split-floating-gate memory for neuromorphic computing and nonlinear activation.

Zhi-Cheng Zhang1, Yuan Li1, Jian Yao2

  • 1The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin, China.

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|January 14, 2026
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Researchers developed a novel split-floating-gate memory device for artificial intelligence (AI) and Internet of Things (IoT) applications. This compact, reconfigurable hardware unifies sensing, computing, and nonlinear processing for efficient intelligent systems.

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Area of Science:

  • * Materials Science and Engineering
  • * Neuromorphic Computing
  • * Artificial Intelligence Hardware

Background:

  • * The expansion of artificial intelligence (AI) and the Internet of Things (IoT) necessitates compact hardware capable of integrated sensing, computing, and nonlinear processing.
  • * Current neuromorphic systems often suffer from limited functionality and rely on heterogeneous integration, hindering scalability and efficiency.

Purpose of the Study:

  • * To introduce a novel multi-modal split-floating-gate memory device.
  • * To demonstrate monolithic integration of in-sensor computing, in-memory computing, and multiple nonlinear activation functions within a single device.
  • * To establish a hardware foundation for scalable, energy-efficient intelligent systems.

Main Methods:

  • * Development of a high-speed, reconfigurable multi-modal split-floating-gate memory architecture.
  • * Programming of charges in spatially separated floating gates to control photoresponsivity and conductance.
  • * Electrical reconfiguration of rectification to emulate ReLU and Sigmoid activation functions.
  • * Implementation of a fully hardware-based sensor-processor system using memory arrays for learning tasks.

Main Results:

  • * The device monolithically integrates sensing, in-memory computing, and multiple nonlinear activation functions (ReLU, Sigmoid).
  • * Non-volatile analog control of photoresponsivity and conductance is achieved through charge programming.
  • * A complete sensor-processor system successfully performed unsupervised and supervised learning tasks in hardware.
  • * Demonstrated high-speed and reconfigurable operation.

Conclusions:

  • * The developed split-floating-gate memory offers a compact and energy-efficient solution for intelligent systems.
  • * Monolithic integration of diverse functionalities enhances scalability and efficiency compared to existing neuromorphic approaches.
  • * This technology provides a reconfigurable hardware foundation for next-generation AI and IoT applications.