Parallelizing analog in-sensor visual processing with arrays of gate-tunable silicon photodetectors
Abstract In-sensor processing of dynamic and static information of visual objects avoids exchanging redundant data between physically separated sensing and computing units, holding promise for computer vision hardware. To this end, gate-tunable photodetectors, if built in a highly scalable array for...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60006-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849325979399356416 |
|---|---|
| author | Zheshun Xiong Wen Liang Meiyue Zhang Dacheng Mao Qiangfei Xia Guangyu Xu |
| author_facet | Zheshun Xiong Wen Liang Meiyue Zhang Dacheng Mao Qiangfei Xia Guangyu Xu |
| author_sort | Zheshun Xiong |
| collection | DOAJ |
| description | Abstract In-sensor processing of dynamic and static information of visual objects avoids exchanging redundant data between physically separated sensing and computing units, holding promise for computer vision hardware. To this end, gate-tunable photodetectors, if built in a highly scalable array form, would lend themselves to large-scale in-sensor visual processing because of their potential in volume production and hence, parallel operation. Here we present two scalable in-sensor visual processing arrays based on dual-gate silicon photodiodes, enabling parallelized event sensing and edge detection, respectively. Both arrays are built in CMOS compatible processes and operated with zero static power. Furthermore, their bipolar analog output captures the amplitude of event-driven light changes and the spatial convolution of optical power densities at the device level, a feature that helps boost their performance in classifying dynamic motions and static images. Capable of processing both temporal and spatial visual information, these retinomorphic arrays suggest a path towards large-scale in-sensor visual processing systems for high-throughput computer vision. |
| format | Article |
| id | doaj-art-9cd82cfb489f4404891e2519baaebbcb |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-9cd82cfb489f4404891e2519baaebbcb2025-08-20T03:48:15ZengNature PortfolioNature Communications2041-17232025-05-0116111310.1038/s41467-025-60006-xParallelizing analog in-sensor visual processing with arrays of gate-tunable silicon photodetectorsZheshun Xiong0Wen Liang1Meiyue Zhang2Dacheng Mao3Qiangfei Xia4Guangyu Xu5Department of Electrical and Computer Engineering, University of Massachusetts, AmherstDepartment of Electrical and Computer Engineering, University of Massachusetts, AmherstDepartment of Electrical and Computer Engineering, University of Massachusetts, AmherstDepartment of Electrical and Computer Engineering, University of Massachusetts, AmherstDepartment of Electrical and Computer Engineering, University of Massachusetts, AmherstDepartment of Electrical and Computer Engineering, University of Massachusetts, AmherstAbstract In-sensor processing of dynamic and static information of visual objects avoids exchanging redundant data between physically separated sensing and computing units, holding promise for computer vision hardware. To this end, gate-tunable photodetectors, if built in a highly scalable array form, would lend themselves to large-scale in-sensor visual processing because of their potential in volume production and hence, parallel operation. Here we present two scalable in-sensor visual processing arrays based on dual-gate silicon photodiodes, enabling parallelized event sensing and edge detection, respectively. Both arrays are built in CMOS compatible processes and operated with zero static power. Furthermore, their bipolar analog output captures the amplitude of event-driven light changes and the spatial convolution of optical power densities at the device level, a feature that helps boost their performance in classifying dynamic motions and static images. Capable of processing both temporal and spatial visual information, these retinomorphic arrays suggest a path towards large-scale in-sensor visual processing systems for high-throughput computer vision.https://doi.org/10.1038/s41467-025-60006-x |
| spellingShingle | Zheshun Xiong Wen Liang Meiyue Zhang Dacheng Mao Qiangfei Xia Guangyu Xu Parallelizing analog in-sensor visual processing with arrays of gate-tunable silicon photodetectors Nature Communications |
| title | Parallelizing analog in-sensor visual processing with arrays of gate-tunable silicon photodetectors |
| title_full | Parallelizing analog in-sensor visual processing with arrays of gate-tunable silicon photodetectors |
| title_fullStr | Parallelizing analog in-sensor visual processing with arrays of gate-tunable silicon photodetectors |
| title_full_unstemmed | Parallelizing analog in-sensor visual processing with arrays of gate-tunable silicon photodetectors |
| title_short | Parallelizing analog in-sensor visual processing with arrays of gate-tunable silicon photodetectors |
| title_sort | parallelizing analog in sensor visual processing with arrays of gate tunable silicon photodetectors |
| url | https://doi.org/10.1038/s41467-025-60006-x |
| work_keys_str_mv | AT zheshunxiong parallelizinganaloginsensorvisualprocessingwitharraysofgatetunablesiliconphotodetectors AT wenliang parallelizinganaloginsensorvisualprocessingwitharraysofgatetunablesiliconphotodetectors AT meiyuezhang parallelizinganaloginsensorvisualprocessingwitharraysofgatetunablesiliconphotodetectors AT dachengmao parallelizinganaloginsensorvisualprocessingwitharraysofgatetunablesiliconphotodetectors AT qiangfeixia parallelizinganaloginsensorvisualprocessingwitharraysofgatetunablesiliconphotodetectors AT guangyuxu parallelizinganaloginsensorvisualprocessingwitharraysofgatetunablesiliconphotodetectors |