无人机载和单兵的能力需求继续推动缩小尺寸、重量和功率(SWaP)和高灵敏度红外(IR)成像的应用,在以前,这些应用不切实际。现在,为了满足这些需求,Attollo工程公司开发了一款1280×1024、5mm像元间距的制冷中波红外(MWIR)传感器,在...无人机载和单兵的能力需求继续推动缩小尺寸、重量和功率(SWaP)和高灵敏度红外(IR)成像的应用,在以前,这些应用不切实际。现在,为了满足这些需求,Attollo工程公司开发了一款1280×1024、5mm像元间距的制冷中波红外(MWIR)传感器,在像元间距方面取得了突破,此外还开发了1280×1024、10mm像元的双波段传感器,在短波红外(SWIR)中具有更高的灵敏度,以利用SWIR现象,其中包括激光定位(laser see spot)功能。这两种传感器都提供MWIR传感功能,但也能够利用Attollo探测器设计的各个方面,实现不同程度的SWIR传感。这类制冷小像元、单波段和双波段红外传感器技术代表了传感器设计和开发的各个方面的进步,我们将讨论Attollo为实现这一能力所做的创新,包括基于III-V族化合物半导体的外延探测器设计、探测器阵列和焦平面阵列制造、低噪声设计、双波段CTIA/DI读出集成电路(ROIC)、真空杜瓦封装、电子和固件设计。在本文中,我们将介绍高清晰度小像元间距MWIR和双波段SWIR/MWIR成像技术在Attollo的现状,它涉及到上述传感器,包括设计和测量数据及成像。展开更多
We demonstrate a high-operating-temperature(HOT)mid-wavelength InAs/GaSb superlattice heterojunction in-frared photodetector grown by metal-organic chemical vapor deposition.High crystalline quality and the near-zero ...We demonstrate a high-operating-temperature(HOT)mid-wavelength InAs/GaSb superlattice heterojunction in-frared photodetector grown by metal-organic chemical vapor deposition.High crystalline quality and the near-zero lattice mis-match of a InAs/GaSb superlattice on an InAs substrate were evidenced by high-resolution X-ray diffraction.At a bias voltage of-0.1 V and an operating temperature of 200 K,the device exhibited a 50%cutoff wavelength of~4.9μm,a dark current dens-ity of 0.012 A/cm^(2),and a peak specific detectivity of 2.3×10^(9) cm·Hz^(1/2)/W.展开更多
Pixel-wise Code Exposure (PCE) camera is one type of compressive sensing camera that has low power consumption and high compression ratio. Moreover, a PCE camera can control individual pixel exposure time that can ena...Pixel-wise Code Exposure (PCE) camera is one type of compressive sensing camera that has low power consumption and high compression ratio. Moreover, a PCE camera can control individual pixel exposure time that can enable high dynamic range. Conventional approaches of using PCE camera involve a time consuming and lossy process to reconstruct the original frames and then use those frames for target tracking and classification. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. Our approach has two parts: tracking and classification. The tracking has been done using YOLO (You Only Look Once) and the classification is achieved using Residual Network (ResNet). Extensive experiments using mid-wave infrared (MWIR) and long-wave infrared (LWIR) videos demonstrated the efficacy of our proposed approach.展开更多
文摘无人机载和单兵的能力需求继续推动缩小尺寸、重量和功率(SWaP)和高灵敏度红外(IR)成像的应用,在以前,这些应用不切实际。现在,为了满足这些需求,Attollo工程公司开发了一款1280×1024、5mm像元间距的制冷中波红外(MWIR)传感器,在像元间距方面取得了突破,此外还开发了1280×1024、10mm像元的双波段传感器,在短波红外(SWIR)中具有更高的灵敏度,以利用SWIR现象,其中包括激光定位(laser see spot)功能。这两种传感器都提供MWIR传感功能,但也能够利用Attollo探测器设计的各个方面,实现不同程度的SWIR传感。这类制冷小像元、单波段和双波段红外传感器技术代表了传感器设计和开发的各个方面的进步,我们将讨论Attollo为实现这一能力所做的创新,包括基于III-V族化合物半导体的外延探测器设计、探测器阵列和焦平面阵列制造、低噪声设计、双波段CTIA/DI读出集成电路(ROIC)、真空杜瓦封装、电子和固件设计。在本文中,我们将介绍高清晰度小像元间距MWIR和双波段SWIR/MWIR成像技术在Attollo的现状,它涉及到上述传感器,包括设计和测量数据及成像。
基金supported partly by the Natural Science Foundation of China with Grant No.61874179,No.61804161,No.61975121 and No.61605236partly by the National Key Research and Development Program of China(No.2019YFB2203400)。
文摘We demonstrate a high-operating-temperature(HOT)mid-wavelength InAs/GaSb superlattice heterojunction in-frared photodetector grown by metal-organic chemical vapor deposition.High crystalline quality and the near-zero lattice mis-match of a InAs/GaSb superlattice on an InAs substrate were evidenced by high-resolution X-ray diffraction.At a bias voltage of-0.1 V and an operating temperature of 200 K,the device exhibited a 50%cutoff wavelength of~4.9μm,a dark current dens-ity of 0.012 A/cm^(2),and a peak specific detectivity of 2.3×10^(9) cm·Hz^(1/2)/W.
文摘Pixel-wise Code Exposure (PCE) camera is one type of compressive sensing camera that has low power consumption and high compression ratio. Moreover, a PCE camera can control individual pixel exposure time that can enable high dynamic range. Conventional approaches of using PCE camera involve a time consuming and lossy process to reconstruct the original frames and then use those frames for target tracking and classification. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. Our approach has two parts: tracking and classification. The tracking has been done using YOLO (You Only Look Once) and the classification is achieved using Residual Network (ResNet). Extensive experiments using mid-wave infrared (MWIR) and long-wave infrared (LWIR) videos demonstrated the efficacy of our proposed approach.
