Microscale optoelectronic reservoir networks of halide perovskite for in-sensor computing
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909755534999552 |
|---|---|
| author | de Boer, Jeroen J. Alvarez, Agustin O. Schmidt, Moritz C. Ehrler, Bruno |
| author_facet | de Boer, Jeroen J. Alvarez, Agustin O. Schmidt, Moritz C. Ehrler, Bruno |
| contents | Physical reservoir computing is a promising framework for efficient neuromorphic in and near-sensor computing applications. Here, we demonstrate a multimodal optoelectronic reservoir network based on halide perovskite semiconductor devices, capable of processing both voltage and light inputs. The devices consist of micrometer-sized, asymmetric crossbars covered with a MAPbI3 perovskite film. In a network, we simulate the performance by transforming MNIST images and videos based on the NMNIST dataset using 4-bit inputs and training linear readout layers for classification. We demonstrate multimodal networks capable of processing both voltage and light inputs, reaching mean accuracies up to 95.3 p/m 0.1% and 87.8 p/m 0.1% for image and video classification, respectively. We observed only minor deterioration due to measurement noise. The networks significantly outperformed linear classifier references, by 3.1% for images and 14.6% for video. We show that longer retention times benefit classification accuracy for single-mode networks, and give guidelines for choosing optimal experimental parameters. Moreover, the microscale device architecture lends itself well to further downscaling in high-density sensor arrays, making the devices ideal for efficient in-sensor computing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19916 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Microscale optoelectronic reservoir networks of halide perovskite for in-sensor computing de Boer, Jeroen J. Alvarez, Agustin O. Schmidt, Moritz C. Ehrler, Bruno Materials Science Physical reservoir computing is a promising framework for efficient neuromorphic in and near-sensor computing applications. Here, we demonstrate a multimodal optoelectronic reservoir network based on halide perovskite semiconductor devices, capable of processing both voltage and light inputs. The devices consist of micrometer-sized, asymmetric crossbars covered with a MAPbI3 perovskite film. In a network, we simulate the performance by transforming MNIST images and videos based on the NMNIST dataset using 4-bit inputs and training linear readout layers for classification. We demonstrate multimodal networks capable of processing both voltage and light inputs, reaching mean accuracies up to 95.3 p/m 0.1% and 87.8 p/m 0.1% for image and video classification, respectively. We observed only minor deterioration due to measurement noise. The networks significantly outperformed linear classifier references, by 3.1% for images and 14.6% for video. We show that longer retention times benefit classification accuracy for single-mode networks, and give guidelines for choosing optimal experimental parameters. Moreover, the microscale device architecture lends itself well to further downscaling in high-density sensor arrays, making the devices ideal for efficient in-sensor computing. |
| title | Microscale optoelectronic reservoir networks of halide perovskite for in-sensor computing |
| topic | Materials Science |
| url | https://arxiv.org/abs/2508.19916 |