Electrically Reconfigurable Floating Gate Optoelectronic Synaptic Pixels for In-sensor Convolutional Image Feature Extraction with Built-in Contrast Enhancement

) as the photoactive channel and bilayer graphene as the floating gate, enabling high optical responsivity even under low-light conditions. The device exhibits a superlinear photoresponse that intrinsically enhances image contrast during sensing. Importantly, the device supports low-voltage, circuit-friendly analog conductance modulation through fully electrical programming, eliminating the need for optical potentiation and simplifying array implementation. The codesigned architecture encodes 4-bit light-intensity-dependent information (16 levels) with strong robustness against cycle-to-cycle and device-to-device variations. Furthermore, we demonstrate in-pixel convolutional operations, including edge detection, image sharpening, and Gaussian blurring. These results highlight the computational versatility of the FG-OS array and establish a scalable pathway toward in-sensor processing and single-layer CNN architectures for intelligent vision systems.

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Publication Details

Journal
ACS Nano
Published
2026-05-11
DOI
https://doi.org/10.1021/acsnano.6c03713
Primary Topic
CCD and CMOS Imaging Sensors
Type
article
Field-Weighted Citation Impact
0.00

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article

Electrically Reconfigurable Floating Gate Optoelectronic Synaptic Pixels for In-sensor Convolutional Image Feature Extraction with Built-in Contrast Enhancement

Shahin Hashemkhani, Joan Marie Redwing, Arijit Sarkar, J T Chen et al.
ACS Nano
CCD and CMOS Imaging Sensors
article

Electrically Reconfigurable Floating Gate Optoelectronic Synaptic Pixels for In-sensor Convolutional Image Feature Extraction with Built-in Contrast Enhancement

Shahin Hashemkhani, Joan Marie Redwing, Arijit Sarkar, J T Chen, C H Chen, Tania Roy, Rajkumar Kubendran, Md Sazzadur Rahman
article en

Abstract

) as the photoactive channel and bilayer graphene as the floating gate, enabling high optical responsivity even under low-light conditions. The device exhibits a superlinear photoresponse that intrinsically enhances image contrast during sensing. Importantly, the device supports low-voltage, circuit-friendly analog conductance modulation through fully electrical programming, eliminating the need for optical potentiation and simplifying array implementation. The codesigned architecture encodes 4-bit light-intensity-dependent information (16 levels) with strong robustness against cycle-to-cycle and device-to-device variations. Furthermore, we demonstrate in-pixel convolutional operations, including edge detection, image sharpening, and Gaussian blurring. These results highlight the computational versatility of the FG-OS array and establish a scalable pathway toward in-sensor processing and single-layer CNN architectures for intelligent vision systems.

ACS Nano
Pennsylvania State University (US), University of Pittsburgh (US), Duke University (US), Crystal Research (United States) (US)
National Science Foundation Graduate Research Fellowship Program
Affordable and clean energy
Openalex Percentile: Top 9%
CCD and CMOS Imaging Sensors
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