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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714942
• 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）图像传感器的系统中的最新突破已经导致了前所未有的成像和检测能力。围绕场景中的光强度、功耗、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。我们的工作性质是设计和原型传感器具有这些特性。拟议的工作旨在：1）显著提高在弱光和强光的极端可能同时存在的环境中和在同一场景中感知和监视快速移动的物体的能力。该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，这是一个影响生产时间和质量的昂贵问题。2)开发高灵敏度的光学过滤器，当放置在传感器表面时，允许在不同的pH值和温度条件下检测生物物质。在我们的第一个应用程序测试，面筋检测，这项研究预计将导致成像过滤器设计的其他过敏原在中期（3-5年）。这项工作的第一个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）创建最先进的快速灵活算法，降低显示图像传感器检测到的各种像素值所需的计算能力。这样做的目的是大大提高图像质量，同时减少时间和电力需求。具有多种工业用途（例如：安全，生物识别，诊断），这一重要的成像工具将作为应用程序提供，并可以集成到现有的传感器技术平台。这三种技术，无论是单独使用还是结合使用，都可立即应用于不同的部门（例如：能源、环境、制造业、健康）。我们正在与这些部门的合作伙伴合作，开发将纳入拟议技术的设备，为加拿大的努力提供技术领导力，HQP以及社会和经济价值。