Swiftly accessible retinomorphic hardware for in-sensor image preprocessing and recognition: IGZO-based neuro-inspired optical image sensor arrays with metallic sensitization island
In-optical-sensor computing architectures based on neuro-inspired optical sensor arrays have become key milestones for in-sensor artificial intelligence (AI) technology, enabling intelligent vision sensing and extensive data processing. These architectures must demonstrate potential advantages in te...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | International Journal of Extreme Manufacturing |
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| Online Access: | https://doi.org/10.1088/2631-7990/adebbe |
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| author | Kyungmoon Kwak Kyungho Park Jae Seong Han Byung Ha Kang Dong Hyun Choi Kunho Moon Seok Min Hong Gwan In Kim Ju Hyun Lee Hyun Jae Kim |
| author_facet | Kyungmoon Kwak Kyungho Park Jae Seong Han Byung Ha Kang Dong Hyun Choi Kunho Moon Seok Min Hong Gwan In Kim Ju Hyun Lee Hyun Jae Kim |
| author_sort | Kyungmoon Kwak |
| collection | DOAJ |
| description | In-optical-sensor computing architectures based on neuro-inspired optical sensor arrays have become key milestones for in-sensor artificial intelligence (AI) technology, enabling intelligent vision sensing and extensive data processing. These architectures must demonstrate potential advantages in terms of mass production and complementary metal oxide semiconductor compatibility. Here, we introduce a visible-light-driven neuromorphic vision system that integrates front-end retinomorphic photosensors with a back-end artificial neural network (ANN), employing a single neuro-inspired indium-gallium-zinc-oxide phototransistor (NIP) featuring an aluminum sensitization layer (ASL). By methodically adjusting the ASL coverage on IGZO phototransistors, a fast-switching response-type and a synaptic response-type of IGZO phototransistors are successfully developed. Notably, the fabricated NIP shows a remarkable retina-like photoinduced synaptic plasticity under wavelengths up to 635 nm, with over 256-states, weight update nonlinearity below 0.1, and a dynamic range of 64.01. Owing to this technology, a 6 × 6 neuro-inspired optical image sensor array with the NIP can perform highly integrated sensing, memory, and preprocessing functions, including contrast enhancement, and handwritten digit image recognition. The demonstrated prototype highlights the potential for efficient hardware implementations in in-sensor AI technologies. |
| format | Article |
| id | doaj-art-e058f4fcf1fe4fc297da27d56ce93f00 |
| institution | DOAJ |
| issn | 2631-7990 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | International Journal of Extreme Manufacturing |
| spelling | doaj-art-e058f4fcf1fe4fc297da27d56ce93f002025-08-20T03:12:02ZengIOP PublishingInternational Journal of Extreme Manufacturing2631-79902025-01-017606550410.1088/2631-7990/adebbeSwiftly accessible retinomorphic hardware for in-sensor image preprocessing and recognition: IGZO-based neuro-inspired optical image sensor arrays with metallic sensitization islandKyungmoon Kwak0Kyungho Park1Jae Seong Han2Byung Ha Kang3Dong Hyun Choi4Kunho Moon5Seok Min Hong6Gwan In Kim7Ju Hyun Lee8Hyun Jae Kim9https://orcid.org/0000-0002-6879-9256School of Electrical and Electronic Engineering, Yonsei University , 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of KoreaSchool of Electrical and Electronic Engineering, Yonsei University , 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of KoreaSchool of Electrical and Electronic Engineering, Yonsei University , 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of KoreaDepartment of Chemical Engineering, Massachusetts Institute of Technology , Cambridge, Massachusetts 02139, United States of AmericaSchool of Electrical and Electronic Engineering, Yonsei University , 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of KoreaSchool of Electrical and Electronic Engineering, Yonsei University , 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of KoreaSchool of Electrical and Electronic Engineering, Yonsei University , 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of KoreaDepartment of Electrical and Computer Engineering, University of Illinois Urbana-Champaign , Urbana, Illinois 61801, United States of AmericaSchool of Electrical and Electronic Engineering, Yonsei University , 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of KoreaSchool of Electrical and Electronic Engineering, Yonsei University , 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of KoreaIn-optical-sensor computing architectures based on neuro-inspired optical sensor arrays have become key milestones for in-sensor artificial intelligence (AI) technology, enabling intelligent vision sensing and extensive data processing. These architectures must demonstrate potential advantages in terms of mass production and complementary metal oxide semiconductor compatibility. Here, we introduce a visible-light-driven neuromorphic vision system that integrates front-end retinomorphic photosensors with a back-end artificial neural network (ANN), employing a single neuro-inspired indium-gallium-zinc-oxide phototransistor (NIP) featuring an aluminum sensitization layer (ASL). By methodically adjusting the ASL coverage on IGZO phototransistors, a fast-switching response-type and a synaptic response-type of IGZO phototransistors are successfully developed. Notably, the fabricated NIP shows a remarkable retina-like photoinduced synaptic plasticity under wavelengths up to 635 nm, with over 256-states, weight update nonlinearity below 0.1, and a dynamic range of 64.01. Owing to this technology, a 6 × 6 neuro-inspired optical image sensor array with the NIP can perform highly integrated sensing, memory, and preprocessing functions, including contrast enhancement, and handwritten digit image recognition. The demonstrated prototype highlights the potential for efficient hardware implementations in in-sensor AI technologies.https://doi.org/10.1088/2631-7990/adebberetinomorphic hardwarein-sensor preprocessingimage recognitionneuro-inspired optical sensorsindium-gallium-zinc-oxidemetallic sensitization layer |
| spellingShingle | Kyungmoon Kwak Kyungho Park Jae Seong Han Byung Ha Kang Dong Hyun Choi Kunho Moon Seok Min Hong Gwan In Kim Ju Hyun Lee Hyun Jae Kim Swiftly accessible retinomorphic hardware for in-sensor image preprocessing and recognition: IGZO-based neuro-inspired optical image sensor arrays with metallic sensitization island International Journal of Extreme Manufacturing retinomorphic hardware in-sensor preprocessing image recognition neuro-inspired optical sensors indium-gallium-zinc-oxide metallic sensitization layer |
| title | Swiftly accessible retinomorphic hardware for in-sensor image preprocessing and recognition: IGZO-based neuro-inspired optical image sensor arrays with metallic sensitization island |
| title_full | Swiftly accessible retinomorphic hardware for in-sensor image preprocessing and recognition: IGZO-based neuro-inspired optical image sensor arrays with metallic sensitization island |
| title_fullStr | Swiftly accessible retinomorphic hardware for in-sensor image preprocessing and recognition: IGZO-based neuro-inspired optical image sensor arrays with metallic sensitization island |
| title_full_unstemmed | Swiftly accessible retinomorphic hardware for in-sensor image preprocessing and recognition: IGZO-based neuro-inspired optical image sensor arrays with metallic sensitization island |
| title_short | Swiftly accessible retinomorphic hardware for in-sensor image preprocessing and recognition: IGZO-based neuro-inspired optical image sensor arrays with metallic sensitization island |
| title_sort | swiftly accessible retinomorphic hardware for in sensor image preprocessing and recognition igzo based neuro inspired optical image sensor arrays with metallic sensitization island |
| topic | retinomorphic hardware in-sensor preprocessing image recognition neuro-inspired optical sensors indium-gallium-zinc-oxide metallic sensitization layer |
| url | https://doi.org/10.1088/2631-7990/adebbe |
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