Modeling and Analysis of Capacitive Relaxation Quenching in a Single Photon Avalanche Diode (SPAD) Applied to a CMOS Image Sensor

Robust Pixel Design Methodologies for a Vertical Avalanche Photodiode (VAPD)-Based CMOS Image Sensor

We present robust pixel design methodologies for a vertical avalanche photodiode-based CMOS image sensor, taking account of three critical practical factors: (i) "guard-ring-free" pixel isolation layout, (ii) device characteristics "insensitive" to applied voltage and temperature, and (iii) stable operation subject to intense light exposure. The "guard-ring-free" pixel design is established by resolving the tradeoff relationship between electric field concentration and pixel isolation. The effectiveness of the optimization strategy is validated both by simulation and experiment. To realize insensitivity to voltage and temperature variations, a global feedback resistor is shown to effectively suppress variations in device characteristics such as photon detection efficiency and dark count rate. An in-pixel overflow transistor is also introduced to enhance the resistance to strong illumination. The robustness of the fabricated VAPD-CIS is verified by characterization of 122 different chips and through a high-temperature and intense-light-illumination operation test with 5 chips, conducted at 125 °C for 1000 h subject to 940 nm light exposure equivalent to 10 kLux.

Insights into the interaction of azinphos-methyl with bovine serum albumin: experimental and molecular docking studies

In the present study, combining spectroscopic and molecular modeling techniques has been used to analyze azinphos-methyl binding properties, as an organophosphorus pesticide, to bovine serum albumin. The quenching interaction of azinphos-methyl with bovine serum albumin was investigated in an appropriate physiological state (pH = 7.4). Fluorescence spectroscopy, UV-visible spectroscopy, circular dichroism (CD) and Fourier transform infrared spectroscopy (FTIR). Findings showed differences in the secondary protein structure microenvironment following interaction with azinphos-methyl. The results from spectroscopic experiments suggest that azinphos-methyl binds to bovine serum albumin residues with a binding constant in the range of 0.099 × 10⁵-0.209 × 10⁵ M^-1 in one binding site (Tyr 160). The experimental results are supported by computational techniques such as docking using a bovine serum albumin crystal model. The results show that azinphos-methyl is linked to the site I of bovine serum albumin (in subdomain IB), and the result was in accordance with the experimental result. Based on the negative ΔG°, ΔH° and ΔS° values, the binding between azinphos-methyl and bovine serum albumin was spontaneous, and docking studies confirmed hydrogen bonding and van der Waals forces between them.Communicated by Ramaswamy H. Sarma.

Recognition Method of Corn and Rice Crop Growth State Based on Computer Image Processing Technology

The agriculture field is one of the most important fields where computational techniques play an imperative role for decision-making whether it is the automation of watering of plants, controlling of humidity levels, and detection of plant diseases and growth of plants. There are problems in the conventional methods where newer computational techniques and image processing methods are not used to keep track of growth of plants. The traditional image capturing and processing models have problems of large image segmentation error, excessive feature extraction time, and poor recognition output. In order to overcome the problems in the traditional plant growth methods based on image processing automations, computer image processing with computational method has been proposed to analyze the plant growth by utilizing state recognition method for corn and rice crops. An image acquisition platform is established on the basis of CMOS image sensor for crop image acquisition. The binary processing is performed, and then the images are segmented to reduce error of segmentation results in the traditional methods. To extract image features of corn and rice crops, convolution neural network (CNN) with newer architecture is used. According to contour information of images, the block wavelet transform method is used for feature adaptive matching. The binary tree structure is used to divide the growth period of corn and rice crops. The fuzzy mathematical model is also devised to identify the characteristics of crops in different growth periods and to complete the identification of growth state. Experimental results show that the proposed method effectively improves problems of traditional methods with better image recognition effect and reduces the time of feature recognition. The time to extract features by the proposed method is 1.4 seconds, whereas comparative methods such as random forest (RF) take 3.8 s and other traditional techniques take 4.9 s. Segmentation result error of the recognition method is also reduced significantly.