Toward Enhanced Biomimetic Artificial Visual Systems Based on Organic Heterojunction Optoelectronic Synaptic Transistors
Abstract: Artificial visual systems, inspired by the human eye, hold significant potential in artificial intelligence. Optoelectronic synapses, integrating image perception, processing, and memory in a single device, offer promising solutions. The human eye exhibits different recognition accuracies for objects under varying light conditions. Therefore, a more biomimetic visual system is needed to better fit actual application scenarios. Here, an organic heterojunction‐based optoelectronic synaptic transistor (OHOST) is proposed to enhance biomimetic artificial visual systems. By utilizing the excellent carrier capture ability of core‐multi‐shell quantum dots (QDs) and the high exciton dissociation efficiency of heterojunction interfaces, the device achieves a recognition capability under different light intensities closely resembling that of the human eye. Under optimal light intensity, the recognition accuracy for the modified national institute of standards and technology (MNIST) dataset can reach 91.52%. Nevertheless, under both low and high light intensities, the accuracy drops to a low level. This work pushes the development of artificial visual systems toward higher levels of biomimicry.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Toward Enhanced Biomimetic Artificial Visual Systems Based on Organic Heterojunction Optoelectronic Synaptic Transistors ; day:17 ; month:09 ; year:2024 ; extent:10
Advanced electronic materials ; (17.09.2024) (gesamt 10)
- Urheber
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Wang, Haonan
Chen, Wandi
Su, Wenjuan
Zou, Zhenyou
Weng, Shuchen
Zhou, Xiongtu
Wu, Chaoxing
Guo, Tailiang
Zhang, Yongai
- DOI
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10.1002/aelm.202400632
- URN
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urn:nbn:de:101:1-2409181409225.975120809502
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:25 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Wang, Haonan
- Chen, Wandi
- Su, Wenjuan
- Zou, Zhenyou
- Weng, Shuchen
- Zhou, Xiongtu
- Wu, Chaoxing
- Guo, Tailiang
- Zhang, Yongai
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