scholarly journals Automatic Range Adjustment of the Fluorescence Immunochromatographic Assay Based on Image Processing

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 209 ◽  
Author(s):  
Ruixin Jiang ◽  
Huihuang Wu ◽  
Jianpeng Yang ◽  
Haiyan Jiang ◽  
Min Du ◽  
...  
Keyword(s):  
Image Processing ◽  
Point Of Care ◽  
Strong Stability ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Image Sensors ◽  
Oxide Semiconductor ◽  
Detection Sensitivity ◽  
Immunochromatographic Assay ◽  
Detection Range

As an emerging technology, fluorescence immunochromatographic assay (FICA) has the advantages of high sensitivity, strong stability and specificity, which is widely used in the fields of medical testing, food safety and environmental monitoring. The FICA reader based on image processing meets the needs of point-of-care testing because of its simple operation, portability and fast detection speed. However, the image gray level of common image sensors limits the detection range of the FICA reader, and high-precision image sensors are expensive, which is not conducive to the popularization of the instrument. In this paper, FICA strips’ image was collected using a common complementary metal oxide semiconductor (CMOS) image sensor and a range adjustment mechanism was established to automatically adjust the exposure time of the CMOS image sensor to achieve the effect of range expansion. The detection sensitivity showed a onefold increase, and the upper detection limit showed a twofold increase after the proposed method was implemented. In addition, in the experiments of linearity and accuracy, the fitting degree (R2) of the fitted curves both reached 0.999. Therefore, the automatic range adjustment method can obviously improve the detection range of the FICA reader based on image processing.

scholarly journals Complementary Metal-Oxide Semiconductor (CMOS) Image Sensor: An Insight as a Point-of-Care Label-Free Immunosensor

2010 ◽  
Vol 26 (12) ◽  
pp. 1215-1217 ◽  
Author(s):  
Karthikeyan KANDASAMY ◽  
Mohana MARIMUTHU ◽  
Gun Yong SUNG ◽  
Chang Geun AHN ◽  
Sanghyo KIM
Keyword(s):  
Metal Oxide ◽  
Point Of Care ◽  
Complementary Metal Oxide Semiconductor ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Label Free

scholarly journals Proximity Gettering Design of Hydrocarbon–Molecular–Ion–Implanted Silicon Wafers Using Dark Current Spectroscopy for CMOS Image Sensors

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2073 ◽  
Author(s):  
Kazunari Kurita ◽  
Takeshi Kadono ◽  
Satoshi Shigematsu ◽  
Ryo Hirose ◽  
Ryosuke Okuyama ◽  
...  
Keyword(s):  
Dark Current ◽  
Image Sensor ◽  
Image Sensors ◽  
Silicon Wafers ◽  
Device Fabrication ◽  
Oxide Semiconductor ◽  
Device Performance ◽  
Molecular Ion ◽  
Cmos Image Sensors ◽  
Ion Implanted

We developed silicon epitaxial wafers with high gettering capability by using hydrocarbon–molecular–ion implantation. These wafers also have the effect of hydrogen passivation on process-induced defects and a barrier to out-diffusion of oxygen of the Czochralski silicon (CZ) substrate bulk during Complementary metal-oxide-semiconductor (CMOS) device fabrication processes. We evaluated the electrical device performance of CMOS image sensor fabricated on this type of wafer by using dark current spectroscopy. We found fewer white spot defects compared with those of intrinsic gettering (IG) silicon wafers. We believe that these hydrocarbon–molecular–ion–implanted silicon epitaxial wafers will improve the device performance of CMOS image sensors.

scholarly journals Low-Cost 2D Index and Straightness Measurement System Based on a CMOS Image Sensor

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5461 ◽  
Author(s):  
Alain Küng ◽  
Benjamin A. Bircher ◽  
Felix Meli
Keyword(s):  
Measurement System ◽  
Degrees Of Freedom ◽  
Low Cost ◽  
Complementary Metal Oxide Semiconductor ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Oxide Semiconductor ◽  
Measurement Systems ◽  
Starting Point ◽  
Sensor Properties

Accurate traceable measurement systems often use laser interferometers for position measurements in one or more dimensions. Since interferometers provide only incremental information, they are often combined with index sensors to provide a stable reference starting point. Straightness measurements are important for machine axis correction and for systems having several degrees of freedom. In this paper, we investigate the accuracy of an optical two-dimensional (2D) index sensor, which can also be used in a straightness measurement system, based on a fiber-coupled, collimated laser beam pointing onto an image sensor. Additionally, the sensor can directly determine a 2D position over a range of a few millimeters. The device is based on a simple and low-cost complementary metal–oxide–semiconductor (CMOS) image sensor chip and provides sub-micrometer accuracy. The system is an interesting alternative to standard techniques and can even be implemented on machines for real-time corrections. This paper presents the developed sensor properties for various applications and introduces a novel error separation method for straightness measurements.

scholarly journals Super Field-of-View Lensless Camera by Coded Image Sensors

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1329 ◽  
Author(s):  
Tomoya Nakamura ◽  
Keiichiro Kagawa ◽  
Shiho Torashima ◽  
Masahiro Yamaguchi
Keyword(s):  
Imaging System ◽  
Complementary Metal Oxide Semiconductor ◽  
Image Sensor ◽  
Image Sensors ◽  
Computational Imaging ◽  
Field Of View ◽  
Oxide Semiconductor ◽  
Back Side ◽  
Cmos Image Sensors ◽  
Rich Information

A lensless camera is an ultra-thin computational-imaging system. Existing lensless cameras are based on the axial arrangement of an image sensor and a coding mask, and therefore, the back side of the image sensor cannot be captured. In this paper, we propose a lensless camera with a novel design that can capture the front and back sides simultaneously. The proposed camera is composed of multiple coded image sensors, which are complementary-metal-oxide-semiconductor (CMOS) image sensors in which air holes are randomly made at some pixels by drilling processing. When the sensors are placed facing each other, the object-side sensor works as a coding mask and the other works as a sparsified image sensor. The captured image is a sparse coded image, which can be decoded computationally by using compressive sensing-based image reconstruction. We verified the feasibility of the proposed lensless camera by simulations and experiments. The proposed thin lensless camera realized super-field-of-view imaging without lenses or coding masks and therefore can be used for rich information sensing in confined spaces. This work also suggests a new direction in the design of CMOS image sensors in the era of computational imaging.

scholarly journals An Optical Filter-Less CMOS Image Sensor with Differential Spectral Response Pixels for Simultaneous UV-Selective and Visible Imaging

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 13
Author(s):  
Yhang Ricardo Sipauba Carvalho da Silva ◽  
Rihito Kuroda ◽  
Shigetoshi Sugawa
Keyword(s):  
Visible Light ◽  
Spectral Sensitivity ◽  
Dynamic Range ◽  
Uv Light ◽  
Spectral Response ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Oxide Semiconductor ◽  
Conversion Gain ◽  
Uv Sensitivity

This paper presents a complementary metal-oxide-semiconductor (CMOS) image sensor (CIS) capable of capturing UV-selective and visible light images simultaneously by a single exposure and without employing optical filters, suitable for applications that require simultaneous UV and visible light imaging, or UV imaging in variable light environment. The developed CIS is composed by high and low UV sensitivity pixel types, arranged alternately in a checker pattern. Both pixel types were designed to have matching sensitivities for non-UV light. The UV-selective image is captured by extracting the differential spectral response between adjacent pixels, while the visible light image is captured simultaneously by the low UV sensitivity pixels. Also, to achieve high conversion gain and wide dynamic range simultaneously, the lateral overflow integration capacitor (LOFIC) technology was introduced in both pixel types. The developed CIS has a pixel pitch of 5.6 µm and exhibits 172 µV/e− conversion gain, 131 ke− full well capacity (FWC), and 92.3 dB dynamic range. The spectral sensitivity ranges of the high and low UV sensitivity pixels are of 200–750 nm and 390–750 nm, respectively. The resulting sensitivity range after the differential spectral response extraction is of 200–480 nm. This paper presents details regarding the CIS pixels structures, doping profiles, device simulations, and the measurement results for photoelectric response and spectral sensitivity for both pixel types. Also, sample images of UV-selective and visible spectral imaging using the developed CIS are presented.

scholarly journals Design of an Edge-Detection CMOS Image Sensor with Built-in Mask Circuits

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3649
Author(s):  
Minhyun Jin ◽  
Hyeonseob Noh ◽  
Minkyu Song ◽  
Soo Youn Kim
Keyword(s):  
Power Consumption ◽  
Edge Detection ◽  
Low Power ◽  
High Speed ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Frame Rate ◽  
Oxide Semiconductor ◽  
Total Power ◽  
Total Power Consumption

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).

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