碲化镉汞
光电探测器
红外线的
噪音(视频)
Mercury(编程语言)
光电子学
热的
材料科学
量子隧道
碲化镉光电
物理
光学
计算机科学
人工智能
气象学
图像(数学)
程序设计语言
摘要
Infrared (IR) sensors are widely used in thermal imaging and sensor applications. The performance of IR sensor is strongly dependent on the noise currents of the sensor. If characteristics of the noise currents are known prior to the costly and time-consuming sensor production phase, high performance IR sensors could be obtained rapidly and cost effectively. In this study, a p–n long-wave IR mercury cadmium telluride sensor is evaluated at 77 K using a physics-based numerical modeling and simulation approach. Results of the study showed that 1/f noise originating from the trap-assisted tunneling dominates as the cut-off wavelength and the magnitude of the applied reverse bias voltage increase. Copyright © 2013 John Wiley & Sons, Ltd.
科研通智能强力驱动
Strongly Powered by AbleSci AI