Electrically Tunable Persistent Photoconductivity Effect in Epitaxial NiO/ZnO Heterojunctions for Artificial Synaptic Devices and Neuromorphic Computing Applications

We report electrically tunable persistent photoconductivity (PPC) behavior in p-(001)NiO/n-(0001)ZnO epitaxial heterojunctions, which can be exploited to develop efficient optoelectronic synaptic devices. It has been demonstrated that the PPC response time can be varied from a few microseconds to hundreds of seconds by applying bias in both forward and reverse directions. This bias tunable nature of PPC provides much better control over the potentiation and depression of the memory state compared with the conventional neuromorphic devices. It has been found that the typical synaptic behavior, such as paired pulse facilitation, short-to-long-term memory transitions, and learning-forgetting processes, can be mimicked very well. Both the electrical and optical energy consumptions are estimated to be as low as a few 100 nJ per synaptic event. The device demonstrates a reliable and repeatable performance over an extended period. Most interestingly, it has been shown that the device can be programmed for different logic operations such as “AND,” “OR,” and “NOT” with both the illumination and bias as inputs and the current through the device as the output. All these results highlight the prospects of these heterojunctions for the development of next-generation optoelectronic synaptic devices and neuromorphic computing.

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Publication Details

Journal
ACS Applied Electronic Materials
Published
2025-05-14
DOI
https://doi.org/10.1021/acsaelm.5c00313
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73
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article

Electrically Tunable Persistent Photoconductivity Effect in Epitaxial NiO/ZnO Heterojunctions for Artificial Synaptic Devices and Neuromorphic Computing Applications

Bhabani Prasad Sahu, Umakanta Patra, Amandeep Kaur, Subhrajit Sikdar et al.
1 citations
ACS Applied Electronic Materials
Advanced Memory and Neural Computing
0.73
article

Electrically Tunable Persistent Photoconductivity Effect in Epitaxial NiO/ZnO Heterojunctions for Artificial Synaptic Devices and Neuromorphic Computing Applications

Bhabani Prasad Sahu, Umakanta Patra, Amandeep Kaur, Subhrajit Sikdar, Subhabrata Dhar
article en
1 citations

Abstract

We report electrically tunable persistent photoconductivity (PPC) behavior in p-(001)NiO/n-(0001)ZnO epitaxial heterojunctions, which can be exploited to develop efficient optoelectronic synaptic devices. It has been demonstrated that the PPC response time can be varied from a few microseconds to hundreds of seconds by applying bias in both forward and reverse directions. This bias tunable nature of PPC provides much better control over the potentiation and depression of the memory state compared with the conventional neuromorphic devices. It has been found that the typical synaptic behavior, such as paired pulse facilitation, short-to-long-term memory transitions, and learning-forgetting processes, can be mimicked very well. Both the electrical and optical energy consumptions are estimated to be as low as a few 100 nJ per synaptic event. The device demonstrates a reliable and repeatable performance over an extended period. Most interestingly, it has been shown that the device can be programmed for different logic operations such as “AND,” “OR,” and “NOT” with both the illumination and bias as inputs and the current through the device as the output. All these results highlight the prospects of these heterojunctions for the development of next-generation optoelectronic synaptic devices and neuromorphic computing.

ACS Applied Electronic MaterialsVol. 7(10)
Indian Institute of Technology Bombay (IN), University of Sheffield (GB)
Affordable and clean energy
Openalex Percentile: Top 28%
Advanced Memory and Neural Computing
0.73
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Electrically Tunable Persistent Photoconductivity Effect in Epitaxial NiO/ZnO Heterojunctions for Artificial Synaptic Devices and Neuromorphic Computing Applications — Bhabani Prasad Sahu, Umakanta Patra, et al. · ACS Applied Electronic Materials (2025) | TGRS Research Map | TGRS