Application of Signal Processing & Dynamic Modeling Techniques to Modern Microsensors

信号处理应用

• 批准号: 9616085
• 负责人: Thomas McAvoy
• 金额: $ 15.78万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-07-15 至 2001-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9616085&HistoricalAwards=false
• 关键词: Application; Signal; Processing; & Dynamic

ABSTRACT - McAvoy - CTS-9616085 At present most chemical plants are operated without the benefit of real time composition measurements. The reasons include cost, reliability, and robustness of composition sensors. Silicon based microsensors, hold promise for having a major impact on the operation of chemical processes. Each microsensor occupies approximately 400 square microns of surface area, so it is feasible to place several hundred sensors on a single, small chip. Tin dioxide (SnO2) gas microsensors are being developed at the National Institute of Standards and Technology (NIST), with whom the PI's are collaborating. These sensors have several unique features including their small size, approximately 200 micrometers in diameter, their fabrication by a conventional complementary metal oxide semiconductor (CMOS) foundry, and the ability to have their operating temperature changed rapidly, in a cyclic pattern, to optimize their sensitivity to a particular gas species. There are a number of problems that remain to be solved before these sensors can be used to solve process problems. Some of these problems include drift and noise effects, lack of sensitivity to some chemicals, and material problems. The SnO2 microsensors are nonspecific and they can respond to a number of chemical species. The research project involves further investigations of SnO2 microsensors. In using microsensors to broad problems need further study: (1) One can use the sensors to determine whether or not a chemical species is present, which is a problem in classification; and (2) one can use the sensors to determine which chemicals are present as well as their specific concentrations, which is a problem in quantitative analysis. The short range goal of the PI's research is to apply signal processing and dynamic modeling techniques to these microsensors to advance their use in the classification problem. Many applications of microsensors involve classification, e.g. hazardous leak, odor, and fire detection . The signal processing and modeling methods planned have the potential to be expanded to the quantitative analysis problem---thus, the long range goal of the research is to help advance the use of microsensor arrays for solving the quantitative analysis problem.

摘要-麦卡沃伊-CTS-9616085目前大多数化工厂都是在没有实时成分测量的情况下运行的。原因包括组合传感器的成本、可靠性和健壮性。硅基微型传感器有望对化学过程的运行产生重大影响。每个微型传感器占据大约400平方微米的表面积，因此在单个小芯片上放置数百个传感器是可行的。美国国家标准与技术研究所(NIST)正在开发二氧化锡(SnO2)气体微型传感器，PI正在与NIST合作。这些传感器具有几个独特的特点，包括它们的小尺寸，直径约200微米，由传统的互补金属氧化物半导体(CMOS)铸造厂制造，以及能够以循环模式快速改变其工作温度，以优化其对特定气体物种的灵敏度。在这些传感器可以用于解决工艺问题之前，还有许多问题需要解决。其中一些问题包括漂移和噪音影响，对一些化学品缺乏敏感性，以及材料问题。SnO2微型传感器是非特异性的，它们可以对许多化学物种做出反应。该研究项目涉及对SnO2微传感器的进一步研究。在使用微型传感器进行广泛研究时，需要进一步研究以下问题：(1)人们可以使用传感器来确定是否存在某种化学物质，这是分类上的问题；以及(2)人们可以使用传感器来确定存在哪些化学物质及其具体浓度，这是定量分析中的一个问题。PI研究的短期目标是将信号处理和动态建模技术应用于这些微传感器，以促进它们在分类问题中的应用。微型传感器的许多应用涉及分类，例如危险泄漏、气味和火灾检测。计划中的信号处理和建模方法有可能扩展到定量分析问题-因此，研究的长期目标是帮助促进微传感器阵列的使用，以解决定量分析问题。