In the manufacturing industry, we have always been concerned about process fluctuations. So, what is process fluctuation? What kind of fluctuation will cause quality problems? How big a fluctuation will cause product failure? The semiconductor manufacturing process is complex. What kind of impact will the superposition of process fluctuations in the previous and subsequent processes bring? This article will introduce how to achieve data concatenation from wafer to packaging to finished product testing under the factory big data infrastructure, as well as business applications based on this. How to identify key parameters from dozens, hundreds, or even thousands of input variables and control them, in a targeted manner, to ensure quality and reduce costs, is the difficulty and key point of complex process flows. Commonality analysis can help us a lot in this regard, and can be used for both diagnosis and prediction. To achieve commonality analysis, we need to pay attention to the process from data collation, data connection, algorithm selection, and result analysis, one link after another, and constantly try and verify. We chose JMP data analysis software to help achieve this.
Abstract:
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.
