The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Integrated In-Sensor Computing Optoelectronic Device for Environment-Adaptable Artificial Retina Perception Application
Abstract
With the development and application of artificial intelligence, there is an appeal to the exploitation of various sensors and memories. As the most important perception of human beings, vision occupies more than 80% of all the received information. Inspired by biological eyes, an artificial retina based on 2D Janus MoSSe was fabricated, which could simulate functions of visual perception with electronic/ion and optical comodulation. Furthermore, inspired by human brain, sensing, memory, and neuromorphic computing functions were integrated on one device for multifunctional intelligent electronics, which was beneficial for scalability and high efficiency. Through the formation of faradic electric double layer (EDL) at the metal-oxide/electrolyte interfaces could realize synaptic weight changes. On the basis of the optoelectronic performances, light adaptation of biological eyes, preprocessing, and recognition of handwritten digits were implemented successfully. This work may provide a strategy for the future integrated sensing-memory-processing device for optoelectronic artificial retina perception application.
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Cites
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Radisavljevic, B., Radenovic, A., Brivio, J., Giacometti, V., & Kis, A. (2011) · Nature Nanotechnology · doi:10.1038/nnano.2010.279 · Cited by 14,939
Highly sensitive flexible pressure sensors with microstructured rubber dielectric layers
Mannsfeld, S. C. B., Tee, B. C.-K., Stoltenberg, R. M., Chen, C. V. H.-H., Barman, S., Muir, B. V. O., Sokolov, A. N., Reese, C., & Bao, Z. (2010) · Nature Materials · doi:10.1038/nmat2834 · Cited by 3,291
Janus monolayers of transition metal dichalcogenides
Lu, A.-Y., Zhu, H., Xiao, J., Chuu, C.-P., Han, Y., Chiu, M.-H., Cheng, C.-C., Yang, C.-W., Wei, K.-H., Yang, Y., et al. (2017) · Nature Nanotechnology · doi:10.1038/nnano.2017.100 · Cited by 2,472
Self-citationUltrafast charge transfer in atomically thin MoS2/WS2 heterostructures
Hong, X., Kim, J., Shi, S.-F., Zhang, Y., Jin, C., Sun, Y., Tongay, S., Wu, J., Zhang, Y., & Wang, F. (2014) · Nature Nanotechnology · doi:10.1038/nnano.2014.167 · Cited by 2,301
Approaching the Schottky–Mott limit in van der Waals metal–semiconductor junctions
Liu, Y., Guo, J., Zhu, E., Liao, L., Lee, S.-J., Ding, M., Shakir, I., Gambin, V., Huang, Y., & Duan, X. (2018) · Nature · doi:10.1038/s41586-018-0129-8 · Cited by 2,132
A bioinspired flexible organic artificial afferent nerve
Kim, Y., Chortos, A., Xu, W., Liu, Y., Oh, J. Y., Son, D., Kang, J., Foudeh, A. M., Zhu, C., Lee, Y., et al. (2018) · Science · doi:10.1126/science.aao0098 · Cited by 1,466
Optoelectronic resistive random access memory for neuromorphic vision sensors
Zhou, F., Zhou, Z., Chen, J., Choy, T. H., Wang, J., Zhang, N., Lin, Z., Yu, S., Kang, J., Wong, H.-S. P., et al. (2019) · Nature Nanotechnology · doi:10.1038/s41565-019-0501-3 · Cited by 1,322
Near-sensor and in-sensor computing
Zhou, F., & Chai, Y. (2020) · Nature Electronics · doi:10.1038/s41928-020-00501-9 · Cited by 1,090
Short-Term Memory to Long-Term Memory Transition in a Nanoscale Memristor
Chang, T., Jo, S.-H., & Lu, W. (2011) · ACS Nano · doi:10.1021/nn202983n · Cited by 1,058
Metadata
- Authors
- Meng, J., Wang, T., Zhu, H., Ji, L., Bao, W., Zhou, P., Chen, L., Sun, Q.-Q., & Zhang, D. W.
- Venue
- Nano Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-12-28
- Volume
- 22
- Issue
- 1
- Pages
- 81-89
- ISSN
- 1530-6984 (ISSN-L)
- 1530-6992
- OpenAlex
- W4200527050
- Cited by
- 269
- References
- 56
- FWCI
- 11.31
- Type
- journal article
- Language
- en
- Open access
- closed
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- Meng, J., Wang, T., Zhu, H., Ji, L., Bao, W., Zhou, P., Chen, L., Sun, Q.-Q., & Zhang, D. W. (2022). Integrated In-Sensor Computing Optoelectronic Device for Environment-Adaptable Artificial Retina Perception Application. Nano Letters. https://doi.org/10.1021/acs.nanolett.1c03240
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