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基于低功耗边缘检测电路的计算机视觉传感器设计

The Design of a Computer Vision Sensor Based on a Low-Power Edge Detection Circuit.

作者信息

Lee Suhyeon, Yun Yu Chan, Heu Seung Min, Lee Kyu Hyun, Lee Seung Joon, Lee Kyungmin, Moon Jiin, Lim Hyuna, Jang Taeun, Song Minkyu, Kim Soo Youn

机构信息

Department of System Semiconductor, Dongguk University, Seoul 04620, Republic of Korea.

出版信息

Sensors (Basel). 2025 May 20;25(10):3219. doi: 10.3390/s25103219.

DOI:10.3390/s25103219
PMID:40432010
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12115697/
Abstract

We propose a complementary metal-oxide-semiconductor (CMOS) image sensor (CIS) that performs edge mask computation and detection during the analog-to-digital (A/D) conversion process to output 1-bit edge images. By utilizing the characteristics of the edge that can obtain a 1-bit image, the edge mask and thresholding operations are performed simultaneously during the A/D conversion process, thereby reducing memory capacity along with a high number of frames per second (FPS). Additionally, by implementing a 1-bit analog-to-digital converter (ADC) instead of a high-resolution ADC and counter through the 1-bit edge data obtained from the edge mask operation, both static and dynamic power consumption are reduced. The proposed CIS, fabricated with a one-poly six-metal CIS process with a 4T-active pixel sensor, has a core area of 2.546 mm × 1.923 mm in a chip area of 2.558 mm × 4.3 mm. The total power consumption is 1.52 mW at 23 FPS, with power supplies of 2.8 V and 1.5 V for the analog domain and 1.5 V for the digital domain.

摘要

我们提出了一种互补金属氧化物半导体(CMOS)图像传感器(CIS),它在模数(A/D)转换过程中执行边缘掩码计算和检测,以输出1位边缘图像。通过利用边缘可获得1位图像的特性,在A/D转换过程中同时执行边缘掩码和阈值操作,从而在降低内存容量的同时提高每秒帧数(FPS)。此外,通过使用从边缘掩码操作获得的1位边缘数据实现1位模数转换器(ADC)而非高分辨率ADC和计数器,可降低静态和动态功耗。所提出的CIS采用具有4T有源像素传感器的一 poly 六金属CIS工艺制造,在2.558 mm×4.3 mm的芯片面积中,核心面积为2.546 mm×1.923 mm。在23 FPS时,总功耗为1.52 mW,模拟域电源为2.8 V和1.5 V,数字域电源为1.5 V。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/ecd04f1bf15b/sensors-25-03219-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/1671217e7787/sensors-25-03219-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/884c9ad9f57e/sensors-25-03219-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/ca740ffc090c/sensors-25-03219-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/da8c9e320ef4/sensors-25-03219-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/b0ad2e6f7fca/sensors-25-03219-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/f5c6a77701ae/sensors-25-03219-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/2ed4e2b365a3/sensors-25-03219-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/aebc43c189e4/sensors-25-03219-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/2cc2aa38bb34/sensors-25-03219-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/d28e4d7baab4/sensors-25-03219-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/7a2403dba383/sensors-25-03219-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/ecd04f1bf15b/sensors-25-03219-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/1671217e7787/sensors-25-03219-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/884c9ad9f57e/sensors-25-03219-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/ca740ffc090c/sensors-25-03219-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/da8c9e320ef4/sensors-25-03219-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/b0ad2e6f7fca/sensors-25-03219-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/f5c6a77701ae/sensors-25-03219-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/2ed4e2b365a3/sensors-25-03219-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/aebc43c189e4/sensors-25-03219-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/2cc2aa38bb34/sensors-25-03219-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/d28e4d7baab4/sensors-25-03219-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/7a2403dba383/sensors-25-03219-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3851/12115697/ecd04f1bf15b/sensors-25-03219-g012.jpg

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本文引用的文献

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2
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Sensors (Basel). 2020 Jun 29;20(13):3649. doi: 10.3390/s20133649.
3
Design of an Always-On Image Sensor Using an Analog Lightweight Convolutional Neural Network.使用模拟轻量化卷积神经网络的始终开启图像传感器设计。
Sensors (Basel). 2020 May 30;20(11):3101. doi: 10.3390/s20113101.
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