Advanced CMOS Imager based systems

基于先进 CMOS 成像仪的系统

• 批准号: RGPIN-2020-04059
• 负责人: YadidPecht, Orly
• 金额: $ 3.35万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739649
• 关键词: Advanced; CMOS; Imager; based; systems

Image sensors are basic components of a wide array of technologies such as cell phone cameras and biometric, machine vision and diagnostic/medical devices. Recent breakthroughs in Complementary Metal-Oxide Semiconductor (CMOS) image sensor based systems have led to unprecedented imaging and detection capabilities. Challenges around the intensity of light in a scene, power consumption, miniaturization, accuracy, and the range of information and substances that can be detected and identified, remain. The nature of our work is to design and prototype sensors with these characteristics. The proposed work aims to: 1) significantly improve the ability to sense and monitor rapidly moving objects in an environment where extremes of low and bright light can exist at the same time and in the same scene. Initially aimed at industrial processes such as welding and manufacturing, this machine vision sensor will improve image quality and support process and quality control management. In doing so it is possible to reduce and address, for example, manufacturing defects; a costly problem impacting production time and quality. 2) Develop highly sensitive optical filters which when placed on the sensor surface allow for the detection of biological substances, under varying pH and temperature conditions. Tested on our first application, gluten detection, this research is expected to lead to imaging filter designs for other allergens in the medium term (3-5 years). The first deliverables of this work would permit food manufacturers and individuals to test for allergens during production; a critical health issue. And 3) Create a state-of-the-art fast and flexible algorithm that reduces the computation power required to display a wide range of pixel values detected by an image sensor. The aim here is to vastly improve image quality while reducing time and power demands. Having many industry uses (e.g.: security, biometrics, diagnostics), this important imaging tool will be available as an App, and can be integrated in existing sensor technology platforms. These three technologies, individually or combined, have immediate application in diverse sectors (e.g.: energy, environment, manufacturing, health). Working with partners in these sectors we are developing devices that will incorporate the proposed technologies, providing technology leadership, HQP, and social and economic value to Canadian endeavours.

图像传感器是一系列技术的基本组成部分，如手机摄像头和生物识别、机器视觉和诊断/医疗设备。基于互补金属氧化物半导体(CMOS)图像传感器系统的最新突破带来了前所未有的成像和检测能力。围绕场景中的光线强度、能耗、小型化、准确性以及可以检测和识别的信息和物质的范围的挑战仍然存在。我们工作的本质是设计具有这些特性的传感器并制作其原型。提出的工作旨在：1)显著提高感知和监控快速移动物体的能力，在一个环境中，弱光和强光的极端可以同时存在，并且在同一场景中。这种机器视觉传感器最初针对焊接和制造等工业过程，将提高图像质量，并支持过程和质量控制管理。通过这样做，可以减少和解决例如制造缺陷；一个影响生产时间和质量的代价高昂的问题。2)开发高灵敏度的光学滤光片，当放置在传感器表面时，允许在不同的pH和温度条件下检测生物物质。在我们的第一个应用-面筋检测上进行了测试，这项研究有望在中期(3-5年)内为其他过敏原设计成像过滤器。这项工作的第一批成果将允许食品制造商和个人在生产过程中测试过敏原；这是一个关键的健康问题。以及3)创建一种最先进的快速而灵活的算法，该算法降低了显示图像传感器检测到的大范围像素值所需的计算能力。这样做的目的是在减少时间和电力需求的同时极大地提高图像质量。拥有许多行业用途(例如：安全、生物识别、诊断)，这一重要的成像工具将以App的形式提供，并可以集成到现有的传感器技术平台中。这三种技术单独或结合在一起，可立即应用于不同的部门(例如：能源、环境、制造、卫生)。我们正在与这些领域的合作伙伴合作开发设备，这些设备将结合拟议的技术，为加拿大的努力提供技术领先、HQP以及社会和经济价值。