Speakers: You Fangfang , Manager of Quality Department of Western Digital China Shanghai Co., Ltd.
Speaker: Cindy You , Quality Manager of Western Digital China (Shanghai) Co., Ltd.
the speech topic : Common analysis and quality prediction of the application of big data in the semiconductor manufacturing industry
Topic: Commonality Analysis in Manufacturing Applications of Big Data and Predictive Quality Assessments
Summary :
In manufacturing, we have always been concerned with process fluctuations. So, what is process fluctuation? What kind of fluctuations can cause quality problems? How much fluctuation can cause a product to fail? The semiconductor manufacturing process is complex, and what impact will the superposition of the fluctuations in the front and rear processes bring? This article will introduce how to realize data concatenation from wafer to packaging to finished product testing under the factory big data infrastructure, and the business application on this basis. How to identify key parameters from dozens, hundreds or even thousands of input variables and control them in a targeted manner, not only to ensure quality, but also to reduce costs, this is the difficulty and the key point of complex technological processes. Commonality analysis can be of great help at this point, both for diagnosis and for prediction. To achieve commonality analysis, we need to pay attention to the process of data sorting, data connection, algorithm selection, and result analysis, and keep trying and verifying. We have chosen JMP data analysis software to facilitate 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.
