In situ training of an in-sensor artificial neural network based on ferroelectric photosensors
Abstract In-sensor computing has emerged as an ultrafast and low-power technique for next-generation machine vision. However, in situ training of in-sensor computing systems remains challenging due to the demands for both high-performance devices and efficient programming schemes. Here, we experimen...
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55508-z |
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| Summary: | Abstract In-sensor computing has emerged as an ultrafast and low-power technique for next-generation machine vision. However, in situ training of in-sensor computing systems remains challenging due to the demands for both high-performance devices and efficient programming schemes. Here, we experimentally demonstrate the in situ training of an in-sensor artificial neural network (ANN) based on ferroelectric photosensors (FE-PSs). Our FE-PS exhibits self-powered, fast (<30 μs), and multilevel (>4 bits) photoresponses, as well as long retention (50 days), high endurance (109), high write speed (100 ns), and small cycle-to-cycle and device-to-device variations (~0.66% and ~2.72%, respectively), all of which are desirable for the in situ training. Additionally, a bi-directional closed-loop programming scheme is developed, achieving a precise and efficient weight update for the FE-PS. Using this programming scheme, an in-sensor ANN based on the FE-PSs is trained in situ to recognize traffic signs for commanding a prototype autonomous vehicle. Moreover, this in-sensor ANN operates 50 times faster than a von Neumann machine vision system. This study paves the way for the development of in-sensor computing systems with in situ training capability, which may find applications in new data-streaming machine vision tasks. |
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| ISSN: | 2041-1723 |