| Autor: |
Seungho Song, Changsoon Choi, Jongtae Ahn, Je‐Jun Lee, Jisu Jang, Byoung‐Soo Yu, Jung Pyo Hong, Yong‐Sang Ryu, Yong‐Hoon Kim, Do Kyung Hwang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
InfoMat, Vol 6, Iss 2, Pp n/a-n/a (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2567-3165 |
| DOI: |
10.1002/inf2.12479 |
| Popis: |
Abstract To overcome the intrinsic inefficiency of the von Neumann architecture, neuromorphic devices that perform analog vector–matrix multiplication have been highlighted for achieving power‐ and time‐efficient data processing. In particular, artificial synapses, of which conductance should be programmed to represent the synaptic weights of the artificial neural network, have been intensively researched to realize neuromorphic devices. Here, inspired by excitatory and inhibitory synapses, we develop an artificial optoelectronic synapse that shows both potentiation and depression characteristics triggered only by optical inputs. The design of the artificial optoelectronic synapse, in which excitatory and inhibitory synaptic phototransistors are serially connected, enables these characteristics by spatiotemporally irradiating the phototransistor channels with optical pulses. Furthermore, a negative synaptic weight can be realized without the need for electronic components such as comparators. With such attributes, the artificial optoelectronic synapse is demonstrated to classify three digits with a high recognition rate (98.3%) and perform image preprocessing via analog vector–matrix multiplication. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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