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The traditional public art education model has many drawbacks. After all, this teaching model is the most common teaching model. Most colleges and universities still rely on the traditional teaching mode. This teaching mode is not attractive, boring, and low cost, so the popularity rate is high. The digital interactive design with multimedia courseware as the main body has played a great role in promoting the teaching of public art education in colleges and universities. In this paper, when teachers use multimedia courseware for teaching, because the computer cannot process physical signals, part of the hardware is a converter that converts physical signals such as light and pictures received by the CMOS sensor into digital signals and inputs them to digital signals and analog signals. Through the converter, the two-way transmission of teacher and student information in the teaching process of public art majors from the perspective of diversified public art was realized. At the same time, the information elements of sound, image and text were integrated into the interactive works of public art majors from the perspective of diverse public art, so as to stimulate students’ interest in public art learning. Combined with the application of CMOS image sensor technology in diversified public art teaching, a questionnaire survey was conducted to explore the satisfaction of students in public art education and teaching of colleges and universities by CMOS image sensor technology, 50% of the students agreed with the proposal in this article. In this paper, the diversified public art teaching based on CMOS image sensor technology was derived outside the classroom, and the teaching was related to students’ life, so as to improve the artistic atmosphere of the campus and truly improve the teaching effect of public art education in colleges and universities.

According to the Center for Disease Control and Prevention (CDC), the average human life expectancy is 78.8 years. Specifically, 3.2 million deaths are reported yearly due to heart disease, cancer, Alzheimer’s disease, diabetes, and COVID-19. Diagnosing the disease is mandatory in the current way of living to avoid unfortunate deaths and maintain average life expectancy. CMOS image sensor (CIS) became a prominent technology in assisting the monitoring and clinical diagnosis devices to treat diseases in the medical domain. To address the significance of CMOS image ‘sensors’ usage in disease diagnosis systems, this paper focuses on the CIS incorporated disease diagnosis systems related to vital organs of the human body like the heart, lungs, brain, eyes, intestines, bones, skin, blood, and bacteria cells causing diseases. This literature survey’s main objective is to evaluate the ‘systems’ capabilities and highlight the most potent ones with advantages, disadvantages, and accuracy, that are used in disease diagnosis. This systematic review used PRISMA workflow for study selection methodology, and the parameter-based evaluation is performed on disease diagnosis systems related to the human body’s organs. The corresponding CIS models used in systems are mapped organ-wise, and the data collected over the last decade are tabulated.

Electronic and photonic technologies have transformed our lives—from computing and mobile devices, to information technology and the internet. Our future demands in these fields require innovation in each technology separately, but also depend on our ability to harness their complementary physics through integrated solutions 1 , 2 . This goal is hindered by the fact that most silicon nanotechnologies—which enable our processors, computer memory, communications chips and image sensors—rely on bulk silicon substrates, a cost-effective solution with an abundant supply chain, but with substantial limitations for the integration of photonic functions. Here we introduce photonics into bulk silicon complementary metal–oxide–semiconductor (CMOS) chips using a layer of polycrystalline silicon deposited on silicon oxide (glass) islands fabricated alongside transistors. We use this single deposited layer to realize optical waveguides and resonators, high-speed optical modulators and sensitive avalanche photodetectors. We integrated this photonic platform with a 65-nanometre-transistor bulk CMOS process technology inside a 300-millimetre-diameter-wafer microelectronics foundry. We then implemented integrated high-speed optical transceivers in this platform that operate at ten gigabits per second, composed of millions of transistors, and arrayed on a single optical bus for wavelength division multiplexing, to address the demand for high-bandwidth optical interconnects in data centres and high-performance computing 3 , 4 . By decoupling the formation of photonic devices from that of transistors, this integration approach can achieve many of the goals of multi-chip solutions 5 , but with the performance, complexity and scalability of ‘systems on a chip’ 1 , 6 – 8 . As transistors smaller than ten nanometres across become commercially available 9 , and as new nanotechnologies emerge 10 , 11 , this approach could provide a way to integrate photonics with state-of-the-art nanoelectronics. A way of integrating photonics with silicon nanoelectronics is described, using polycrystalline silicon on glass islands alongside transistors on bulk silicon complementary metal–oxide–semiconductor chips.

Integrated circuits based on complementary metal-oxide–semiconductors (CMOS) are at the heart of the technological revolution of the past 40 years, enabling compact and low-cost microelectronic circuits and imaging systems. However, the diversification of this platform into applications other than microcircuits and visible-light cameras has been impeded by the difficulty to combine semiconductors other than silicon with CMOS. Here, we report the monolithic integration of a CMOS integrated circuit with graphene, operating as a high-mobility phototransistor. We demonstrate a high-resolution, broadband image sensor and operate it as a digital camera that is sensitive to ultraviolet, visible and infrared light (300–2,000 nm). The demonstrated graphene–CMOS integration is pivotal for incorporating 2D materials into the next-generation microelectronics, sensor arrays, low-power integrated photonics and CMOS imaging systems covering visible, infrared and terahertz frequencies. Graphene–quantum dots on CMOS sensor offers broadband imaging.

A lateral overflow integration capacitor (LOFIC) complementary metal oxide semiconductor (CMOS) image sensor can realize high-dynamic-range (HDR) imaging with combination of a low-conversion-gain (LCG) signal for large maximum signal electrons and a high-conversion-gain (HCG) signal for electron-referred noise floor. However, LOFIC-CMOS image sensor requires a two-channel read-out chain for LCG and HCG signals whose polarities are inverted. In order to provide an area-efficient LOFIC-CMOS image sensor, a one-channel read-out chain that can process both HCG and LCG signals is presented in this paper. An up/down double-sampling circuit composed of an inverting amplifier for HCG signals and a non-inverting attenuator for LCG signals can reduce the area of the read-out chain by half compared to the conventional two-channel read-out chain. A test chip is fabricated in a 0.18 μm CMOS process with a metal–insulator–metal (MIM) capacitor, achieving a readout noise of 130 μVrms for the HCG signal and 1.19 V for the LCG input window. The performance is equivalent to 103 dB of the dynamic range with our previous LOFIC pixel in which HCG and LCG conversion gains are, respectively, 160 μV/e− and 10 μV/e−.

In-sensor computing, which integrates sensing, memory and processing functions, has shown substantial potential in artificial vision systems. However, large-scale monolithic integration of in-sensor computing based on emerging devices with complementary metal–oxide–semiconductor (CMOS) circuits remains challenging, lacking functional demonstrations at the hardware level. Here we report a fully integrated 1-kb array with 128 × 8 one-transistor one-optoelectronic memristor (OEM) cells and silicon CMOS circuits, which features configurable multi-mode functionality encompassing three different modes of electronic memristor, dynamic OEM and non-volatile OEM (NV-OEM). These modes are configured by modulating the charge density within the oxygen vacancies via synergistic optical and electrical operations, as confirmed by differential phase-contrast scanning transmission electron microscopy. Using this OEM system, three visual processing tasks are demonstrated: image sensory pre-processing with a recognition accuracy enhanced from 85.7% to 96.1% by the NV-OEM mode, more advanced object tracking with 96.1% accuracy using both dynamic OEM and NV-OEM modes and human motion recognition with a fully OEM-based in-sensor reservoir computing system achieving 91.2% accuracy. A system-level benchmark further shows that it consumes over 20 times less energy than graphics processing units. By monolithically integrating the multi-functional OEMs with Si CMOS, this work provides a cost-effective platform for diverse in-sensor computing applications. This study reports a fully integrated 128 × 8 optoelectronic memristor array with Si complementary metal–oxide–semiconductor circuits, featuring configurable multi-mode functionality. It demonstrates diversified in-sensor computing tasks and consumes 20 times less energy than GPUs.

With the advent of the Internet of Things era, the detection and recognition of moving objects is becoming increasingly important 1 . The current motion detection and recognition (MDR) technology based on the complementary metal oxide semiconductor (CMOS) image sensors (CIS) platform contains redundant sensing, transmission conversion, processing and memory modules, rendering the existing systems bulky and inefficient in comparison to the human retina. Until now, non-memory capable vision sensors have only been used for static targets, rather than MDR. Here, we present a retina-inspired two-dimensional (2D) heterostructure based retinomorphic hardware device with all-in-one perception, memory and computing capabilities for the detection and recognition of moving trolleys. The proposed 2D retinomorphic device senses an optical stimulus to generate progressively tuneable positive/negative photoresponses and memorizes it, combined with interframe differencing computations, to achieve 100% separation detection of moving trichromatic trolleys without ghosting. The detected motion images are fed into a conductance mapped neural network to achieve fast trolley recognition in as few as four training epochs at 10% noise level, outperforming previous results from similar customized datasets. The prototype demonstration of a 2D retinomorphic device with integrated perceptual memory and computation provides the possibility of building compact, efficient MDR hardware. A retina-inspired two-dimensional material based retinomorphic device exhibits all-in-one perception, memory and computing capabilities for motion detection and recognition.

Imaging the polarization of light scattered from an object provides an additional degree of freedom for gaining information from a scene. Conventional polarimeters can be bulky and usually consist of mechanically moving parts (with a polarizer and analyzer setup rotating to reveal the degree of polarization). Rubin et al. designed a metasurface-based full-Stokes compact polarization camera without conventional polarization optics and without moving parts. The results provide a simplified route for polarization imaging. Science , this issue p. eaax1839 A metasurface array is designed that can operate as a polarization camera Recent developments have enabled the practical realization of optical elements in which the polarization of light may vary spatially. We present an extension of Fourier optics—matrix Fourier optics—for understanding these devices and apply it to the design and realization of metasurface gratings implementing arbitrary, parallel polarization analysis. We show how these gratings enable a compact, full-Stokes polarization camera without standard polarization optics. Our single-shot polarization camera requires no moving parts, specially patterned pixels, or conventional polarization optics and may enable the widespread adoption of polarization imaging in machine vision, remote sensing, and other areas.

This letter introduces a novel 10‐bit two‐step single slope analogue‐to‐digital converter architecture featuring high 3‐bit and low 7‐bit quantization stages. It reuses the programmable gain amplifier as the multiplying digital‐to‐analogue converter (MDAC) for residue amplification, enhancing resource efficiency. A on‐demand column‐level MDAC calibration mechanism without redundant bits corrects coarse quantization errors and ensures accurate residue amplification. This work achieves a maximum sampling rate of up to 1 M SPS and an effective number of bits of 9.5 bits, with an signal‐to‐noise ratio of 58 dB, and DNL and INL within 1.5 LSB, making it suitable for high‐performance CMOS image sensors. This paper presents a novel two‐step single slope analogue‐to‐digital converter architecture with high 3‐bit and low 7‐bit quantization stages, utilizing the programmable gain amplifier as the multiplying digital‐to‐analogue converter (MDAC) for efficient residue amplification. A column‐level on‐demand MDAC calibration mechanism corrects quantization errors, improving speed, accuracy, and robustness, achieving an effective number of bits of 9.5 bit with DNL and INL within 1.5 LSB, suitable for high‐performance CMOS image sensors.