تقرير
Ultrasensitive, Ultrafast and Gate-Tunable Two-Dimensional Photodetectors in Ternary Rhombohedral ZnIn2S4 for Optical Neural Networks
| العنوان: | Ultrasensitive, Ultrafast and Gate-Tunable Two-Dimensional Photodetectors in Ternary Rhombohedral ZnIn2S4 for Optical Neural Networks |
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| المؤلفون: | Zhen, Weili, Zhou, Xi, Weng, Shirui, Zhu, Wenka, Zhang, Changjin |
| المصدر: | ACS Appl. Mater. Interfaces 2022, 14, 10, 12571-12582 |
| سنة النشر: | 2022 |
| المجموعة: | Condensed Matter |
| مصطلحات موضوعية: | Condensed Matter - Mesoscale and Nanoscale Physics |
| الوصف: | The demand for high-performance semiconductors in electronics and optoelectronics has prompted the expansion of low-dimensional materials research to ternary compounds. However, photodetectors based on 2D ternary materials usually suffer from large dark currents and slow response, which means increased power consumption and reduced performance. Here we report a systematic study of the optoelectronic properties of well-characterized rhombohedral ZnIn2S4 (R-ZIS) nanosheets which exhibit an extremely low dark current (7 pA at 5 V bias). The superior performance represented by a series of parameters surpasses most 2D counterparts. The ultrahigh specific detectivity (1.8 x 10^14 Jones), comparably short response time ({\tau}_rise = 222 {\mu}s, {\tau}_decay = 158 {\mu}s) and compatibility with high-frequency operation (1000 Hz) are particularly prominent. Moreover, a gate-tunable characteristic is observed, which is attributed to photogating and improves the photoresponse by two orders of magnitude. Gating technique can effectively modulate the photocurrent-generation mechanism from photoconductive effect to dominant photogating. The combination of ultrahigh sensitivity, ultrafast response and high gate tunability makes the R-ZIS phototransistor an ideal device for low-energy-consumption and high-frequency optoelectronic applications, which is further demonstrated by its excellent performance in optical neural networks and promising potential in optical deep learning and computing. Comment: 33 pages, 8 figures, TOC, Supporting Information |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1021/acsami.2c00063 |
| URL الوصول: | http://arxiv.org/abs/2202.11851 |
| رقم الأكسشن: | edsarx.2202.11851 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1021/acsami.2c00063 |
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