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Reconfigurable Hydroxyl Dissociation for Spectrally Decoupled Weight Programming and Photocurrent Computing.

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We developed an optically programmable direct photocurrent computing device using bismuth oxyselenide for efficient AIoT edge vision. This innovation enables low-power, compact sensory systems by integrating processing directly into the sensor.

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bismuth oxyselenideneuromorphic computingoptical neural networkoptoelectronic synapsephotocurrent computingphotogatingprocessing in sensor

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Area of Science:

  • Optoelectronics
  • Materials Science
  • Artificial Intelligence

Background:

  • The Artificial Intelligence of Things (AIoT) requires compact, power-efficient sensory systems.
  • Processing-in-sensor (PIS) offers a solution for edge vision applications by integrating computation within the sensor.
  • Direct photocurrent computing (DPC) utilizes photoresponsivity for in-sensor multiply-accumulate (MAC) operations but faces limitations with electrical weight programming.

Purpose of the Study:

  • To introduce an optically programmable DPC device for enhanced AIoT sensory systems.
  • To overcome the limitations of electrical weight programming in DPC devices.
  • To demonstrate a novel material platform for spectrally decoupled weight programming and photocurrent computing.

Main Methods:

  • Development of a vacancy-modulated bismuth oxyselenide (BOS) material platform.
  • Utilizing reversible surface hydroxyl dissociation triggered by ultraviolet light to reconfigure oxygen vacancy dynamics.
  • Implementation of a BOS array for PIS hardware, enabling coarse classification and pre-processing for complex vision tasks.

Main Results:

  • Demonstration of an optically programmable DPC device based on BOS.
  • Achieved spectrally decoupled weight programming and photocurrent computing.
  • Successful implementation of PIS hardware for low-power classification and pre-processing in a processing-near-sensor (PNS) paradigm.

Conclusions:

  • The optically programmable BOS DPC device offers a promising solution for compact and power-efficient AIoT sensory systems.
  • A hybrid architecture combining PIS and PNS can intelligently balance computational resources for optimal power and performance.
  • This work paves the way for advanced edge AIoT applications requiring sophisticated vision processing.