ArticleNature communications2025
Parallelizing analog in-sensor visual processing with arrays of gate-tunable silicon photodetectors.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Amplitude-controllable event-driven organic photosensors based on ionic-mediated inhibition.Nature materials · 2026Article
- Differential Image Sensor With Decoupled Static and Dynamic Outputs.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- A capacitive photodiode for analogue and digital processing.Nature communications · 2026Article
- Reconfigurable ferroelectric transistor array for embodied neuromorphic vision with hardware-native sensing, computing, and activation.Science advances · 2026Article
- Extreme Illuminated Vision Processing with a Graded Alloyed Perovskite In-sensor Computing Network.Nature communications · 2026Article
- A reconfigurable photosensitive split-floating-gate memory for neuromorphic computing and nonlinear activation.Nature communications · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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Registered trials
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