File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1063/5.0263338
- Scopus: eid_2-s2.0-105002275865
- WOS: WOS:001461244000013
- Find via

Supplementary
- Citations:
- Appears in Collections:
Article: Synaptic solar-blind UV PD based on STO/AlXGa1−XN heterostructure for neuromorphic computing
| Title | Synaptic solar-blind UV PD based on STO/AlXGa1−XN heterostructure for neuromorphic computing |
|---|---|
| Authors | |
| Issue Date | 7-Apr-2025 |
| Publisher | American Institute of Physics |
| Citation | Applied Physics Letters, 2025, v. 126, n. 14, p. 1-8 How to Cite? |
| Abstract | The rapid advancements in artificial intelligence and high-performance computing have emphasized the need for efficient optoelectronic artificial synapses, essential elements in neuromorphic computing. This study proposes a solar-blind ultraviolet (UV) photodetector (PD) based on the SrTiO3/AlXGa1−XN heterostructure to serve as an optoelectronic synapse. Under 265 nm illumination, the device demonstrates a remarkably low dark current of 1.08 × 10−11 A and an impressive peak responsivity of 36.43 A/W at −15 V. Notably, the UV PD functions as an optoelectronic synapse that emulates a biological neuron, simulating the fundamental operations of various biological synapses. Moreover, the research extends to the promising field of neuromorphic computing. The photoelectric artificial synapse device achieved an exceptional 97.91% accuracy rate in the challenging MNIST handwritten digit recognition task, further validating its potential in neural computing applications. |
| Persistent Identifier | http://hdl.handle.net/10722/355837 |
| ISSN | 2023 Impact Factor: 3.5 2023 SCImago Journal Rankings: 0.976 |
| ISI Accession Number ID |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Qi, X. | - |
| dc.contributor.author | Dai, S. | - |
| dc.contributor.author | Chu, C. | - |
| dc.contributor.author | Yu, S. | - |
| dc.contributor.author | Wang, X. | - |
| dc.contributor.author | Yang, S. | - |
| dc.contributor.author | Ling, F.C.C. | - |
| dc.contributor.author | Yang G. | - |
| dc.date.accessioned | 2025-05-17T00:35:24Z | - |
| dc.date.available | 2025-05-17T00:35:24Z | - |
| dc.date.issued | 2025-04-07 | - |
| dc.identifier.citation | Applied Physics Letters, 2025, v. 126, n. 14, p. 1-8 | - |
| dc.identifier.issn | 0003-6951 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/355837 | - |
| dc.description.abstract | The rapid advancements in artificial intelligence and high-performance computing have emphasized the need for efficient optoelectronic artificial synapses, essential elements in neuromorphic computing. This study proposes a solar-blind ultraviolet (UV) photodetector (PD) based on the SrTiO3/AlXGa1−XN heterostructure to serve as an optoelectronic synapse. Under 265 nm illumination, the device demonstrates a remarkably low dark current of 1.08 × 10−11 A and an impressive peak responsivity of 36.43 A/W at −15 V. Notably, the UV PD functions as an optoelectronic synapse that emulates a biological neuron, simulating the fundamental operations of various biological synapses. Moreover, the research extends to the promising field of neuromorphic computing. The photoelectric artificial synapse device achieved an exceptional 97.91% accuracy rate in the challenging MNIST handwritten digit recognition task, further validating its potential in neural computing applications. | - |
| dc.language | eng | - |
| dc.publisher | American Institute of Physics | - |
| dc.relation.ispartof | Applied Physics Letters | - |
| dc.title | Synaptic solar-blind UV PD based on STO/AlXGa1−XN heterostructure for neuromorphic computing | - |
| dc.type | Article | - |
| dc.identifier.doi | 10.1063/5.0263338 | - |
| dc.identifier.scopus | eid_2-s2.0-105002275865 | - |
| dc.identifier.volume | 126 | - |
| dc.identifier.issue | 14 | - |
| dc.identifier.spage | 1 | - |
| dc.identifier.epage | 8 | - |
| dc.identifier.eissn | 1077-3118 | - |
| dc.identifier.isi | WOS:001461244000013 | - |
| dc.identifier.issnl | 0003-6951 | - |
