This website uses Cookies. Click Accept to agree to our website's cookie use as described in our Privacy Policy. Click Preferences to customize your cookie settings.
In the manufacturing industry, we are always concerned about process fluctuations. So what are process fluctuations? What types of fluctuations will result in quality problems? How much volatility will result in product failures? The semiconductor manufacturing process is very complex. After these fluctuations are superimposed on the manufacturing process, what kind of results can we expect? This text will introduce the process of implementing data connectivity from wafer to package to finished products within an industrial big data framework, as well as evaluating business applications on this basis. Being able to identify and control key parameters among dozens, hundreds, or even thousands of input variables while ensuring quality assurance and low costs at the same time is a difficult – and pivotal – part of the complex industrial process flow. Commonality analysis can be very helpful in this area, for the purpose of both diagnosis and prediction. In order to enable commonality analysis, we need to focus on all areas of this process, including data collection, data connectivity, algorithm selection and results analysis. The cycle then repeats and iterates itself, making improvements with each step of experimentation and validation. We have chosen to use JMP analysis software to help us achieve these goals.