ANSYS based design of nano-scaled fringe capacitive sensor

碩士 === 國立雲林科技大學 === 機械工程系碩士班 === 92 === In this paper, I used the theory offered by chi-chuan which is design fringe capacitive sensors can analysis nano-scale by ANSYS. I designed a 1.5-ring fringe capacitive sensors to reduce the multi-ring fringe capacitive sensors model simplest and impoved abil...

Full description

Bibliographic Details
Main Authors: gen-yau Chen, 陳亙佑
Other Authors: Dau-Chung Wang
Format: Others
Language:zh-TW
Published: 2004
Online Access:http://ndltd.ncl.edu.tw/handle/89694625150465803153
Description
Summary:碩士 === 國立雲林科技大學 === 機械工程系碩士班 === 92 === In this paper, I used the theory offered by chi-chuan which is design fringe capacitive sensors can analysis nano-scale by ANSYS. I designed a 1.5-ring fringe capacitive sensors to reduce the multi-ring fringe capacitive sensors model simplest and impoved ability of the sensor. Although the sensitivity of the multi-ring fringe capacitive sensors is better than the sensitivity of the single ring. Relatively the analysis model of multi-ring mode is more difficult than single ring mode, and more software and hardware were afforded. Of course, the fabrication is more difficult. So we need to find the simplest multi-ring fringe capacitive sensors model to improve the sensitivity of the single ring mode. And the result of we got is available. I get a new idea from the conclusion that is studied by some people before me. The conclusion is under the same linear error when we want to the sensor have higher sensitivity, the measurable area had became smaller than other. And the sample must approach to sensor. So we have a new idea with the sensor design how to raise sensitivity availably, also qualify enough measurable area. So when we design the 1.5-ring sensor model, we also do some systematic analysis to find the available design method. And we arrived our expectation. We also find when we do nano-scale analysis by ANSYS. There exists some condition. The condition is the feature size can’t differ so much. And we got some result prove our assumption.