This article presents an ultra-low power counter structure for a column-parallel single-slope analog-to-digital converter (SS-ADC) in CMOS image sensors. The proposed counter employs a two-step logical shift algorithm-based correlated double sampling (CDS) scheme. The logical shift algorithm can reduce parasitic capacitances, driving frequency, and inner toggling nodes by using the minimum number of transistors and a single-direction counter structure. Moreover, the two-step counting and double data rate scheme in the LSB counter can halve the operating clock frequency, resulting in further decreased power consumption. A prototype sensor was fabricated using a 110 nm CMOS image sensor process. The measurement results show that the proposed SS-ADC with a two-step counter consumes 2.4 μW power per column and shows a differential nonlinearity of +0.38/-0.25 LSB and an integral nonlinearity of +0.75/-0.5 LSB. The total power consumption is 2.25 mW for 640 x 480 effective image resolution at 60 frame rates with 3.3 V/1.5 V supply voltage.

This paper presents an always-on Complementary Metal Oxide Semiconductor (CMOS) image sensor (CIS) using an analog convolutional neural network for image classification in mobile applications. To reduce the power consumption as well as the overall processing time, we propose analog convolution circuits for computing convolution, max-pooling, and correlated double sampling operations without operational transconductance amplifiers. In addition, we used the voltage-mode MAX circuit for max pooling in the analog domain. After the analog convolution processing, the image data were reduced by 99.58% and were converted to digital with a 4-bit single-slope analog-to-digital converter. After the conversion, images were classified by the fully connected processor, which is traditionally performed in the digital domain. The measurement results show that we achieved an 89.33% image classification accuracy. The prototype CIS was fabricated in a 0.11 μm 1-poly 4-metal CIS process with a standard 4T-active pixel sensor. The image resolution was 160 × 120, and the total power consumption of the proposed CIS was 1.12 mW with a 3.3 V supply voltage and a maximum frame rate of 120.

In this paper, we propose a complementary metal-oxide-semiconductor (CMOS) image sensor (CIS) that has built-in mask circuits to selectively capture either edge-detection images or normal 8-bit images for low-power computer vision applications. To detect the edges of images in the CIS, neighboring column data are compared in in-column memories after column-parallel analog-to-digital conversion with the proposed mask. The proposed built-in mask circuits are implemented in the CIS without a complex image signal processer to obtain edge images with high speed and low power consumption. According to the measurement results, edge images were successfully obtained with a maximum frame rate of 60 fps. A prototype sensor with 1920 × 1440 resolution was fabricated with a 90-nm 1-poly 5-metal CIS process. The area of the 4-shared 4T-active pixel sensor was 1.4 × 1.4 µm², and the chip size was 5.15 × 5.15 mm². The total power consumption was 9.4 mW at 60 fps with supply voltages of 3.3 V (analog), 2.8 V (pixel), and 1.2 V (digital).

This paper presents a CMOS image sensor (CIS) with built-in lane detection computing circuits for automotive applications. We propose on-CIS processing with an edge detection mask used in the readout circuit of the conventional CIS structure for high-speed lane detection. Furthermore, the edge detection mask can detect the edges of slanting lanes to improve accuracy. A prototype of the proposed CIS was fabricated using a 110 nm CIS process. It has an image resolution of 160 (H) × 120 (V) and a frame rate of 113, and it occupies an area of 5900 μm × 5240 μm. A comparison of its lane detection accuracy with that of existing edge detection algorithms shows that it achieves an acceptable accuracy. Moreover, the total power consumption of the proposed CIS is 9.7 mW at pixel, analog, and digital supply voltages of 3.3, 3.3, and 1.5 V, respectively.

In this paper, we present a multi-resolution mode CMOS image sensor (CIS) for intelligent surveillance system (ISS) applications. A low column fixed-pattern noise (CFPN) comparator is proposed in 8-bit two-step single-slope analog-to-digital converter (TSSS ADC) for the CIS that supports normal, 1/2, 1/4, 1/8, 1/16, 1/32, and 1/64 mode of pixel resolution. We show that the scaled-resolution images enable CIS to reduce total power consumption while images hold steady without events. A prototype sensor of 176 × 144 pixels has been fabricated with a 0.18 μm 1-poly 4-metal CMOS process. The area of 4-shared 4T-active pixel sensor (APS) is 4.4 μm × 4.4 μm and the total chip size is 2.35 mm × 2.35 mm. The maximum power consumption is 10 mW (with full resolution) with supply voltages of 3.3 V (analog) and 1.8 V (digital) and 14 frame/s of frame rates.

In this paper, a flip-flop (FF) that minimizes the transition of internal nodes by using a dual change-sensing scheme is discussed. Further, in order to reduce power consumption, a new technique to eliminate short-circuit currents is described. The proposed dual change-sensing FF (DCSFF) composed of 24T (T: number of transistors) has the lowest dynamic power consumption among conventional FFs, independent of the data activity ratio. According to the measured results with a 65 nm CMOS process, the power consumption of DCSFF is reduced by 98% and 32%, when the data activity is close to 0% and 100%, respectively, compared to that of conventional transmission gate FF. Further, compared to that of change-sensing FF, the power consumption of DCSFF is reduced by 26% when the data activity is close to 100%.