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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2510.13645 |
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| _version_ | 1866915556131602432 |
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| author | Cui, Mengyang Qi, Hongxing Hu, Chengduo Li, Qing |
| author_facet | Cui, Mengyang Qi, Hongxing Hu, Chengduo Li, Qing |
| contents | We investigated the nonlinear phenomena observed in the dark current of BIB (blocked-impurity-band) infrared detectors, including negative differential resistance (NDR) and current oscillations. Our analysis systematically elucidated the intrinsic transport mechanisms in optimized devices, revealing that these anomalies arise from current path clustering induced by structural disorder and impurity band conduction dynamics. Notably, the simulated current-voltage (I-V) characteristics demonstrated strong agreement with experimental measurements across a wide bias range, confirming the validity of our proposed physical model.Furthermore, we developed a transformer-based predictive model using experimental dark current datasets. The model achieved robust performance metrics and this framework enables rapid prediction of dark current trends under varying operational conditions, providing actionable insights for detector optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13645 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analysis and Prediction of Dark Current Mechanisms in Si:P Blocked Impurity Band (BIB) Infrared Detectors Cui, Mengyang Qi, Hongxing Hu, Chengduo Li, Qing Disordered Systems and Neural Networks We investigated the nonlinear phenomena observed in the dark current of BIB (blocked-impurity-band) infrared detectors, including negative differential resistance (NDR) and current oscillations. Our analysis systematically elucidated the intrinsic transport mechanisms in optimized devices, revealing that these anomalies arise from current path clustering induced by structural disorder and impurity band conduction dynamics. Notably, the simulated current-voltage (I-V) characteristics demonstrated strong agreement with experimental measurements across a wide bias range, confirming the validity of our proposed physical model.Furthermore, we developed a transformer-based predictive model using experimental dark current datasets. The model achieved robust performance metrics and this framework enables rapid prediction of dark current trends under varying operational conditions, providing actionable insights for detector optimization. |
| title | Analysis and Prediction of Dark Current Mechanisms in Si:P Blocked Impurity Band (BIB) Infrared Detectors |
| topic | Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2510.13645 |