大数据在半导体制造业的应用之共性分析及质量预测_尤芳芳(Cindy You)
演讲嘉宾： 尤芳芳 ，西部数据中国上海有限公司质量部经理
Speaker: Cindy You , Quality Manager of Western Digital China (Shanghai) Co., Ltd.
在制造业，我们一直关注过程波动。那么，什么叫过程波动？什么样的波动会造成质量问题？多大的波动会引起产品失效？半导体制造工艺流程复杂，前后工序过程波动叠加又会带来什么样的影响？本文将介绍在工厂大数据基础架构下，如何从晶圆到封装再到成品测试实现数据串联，以及在此基础上的业务应用。如何从几十个几百个甚至上千个输入变量中识别关键参数并加以控制，有的放矢，既要保证质量，又要降低成本，这是复杂工艺流程的难点，也是要点。共性分析在这一点上能给我们很大帮助，既可用于诊断，也可用于预测。而要实现共性分析，我们需要注意从数据整理、数据连接，到算法选择、结果解析这些过程中，一环扣一环，不断尝试与验证。我们选择了JMP数据分析软件来助力实现这些。
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.
演讲嘉宾： 尤芳芳 ，西部数据中国上海有限公司质量部经理
Speaker: Cindy You , Quality Manager of Western Digital China (Shanghai) Co., Ltd.
在制造业，我们一直关注过程波动。那么，什么叫过程波动？什么样的波动会造成质量问题？多大的波动会引起产品失效？半导体制造工艺流程复杂，前后工序过程波动叠加又会带来什么样的影响？本文将介绍在工厂大数据基础架构下，如何从晶圆到封装再到成品测试实现数据串联，以及在此基础上的业务应用。如何从几十个几百个甚至上千个输入变量中识别关键参数并加以控制，有的放矢，既要保证质量，又要降低成本，这是复杂工艺流程的难点，也是要点。共性分析在这一点上能给我们很大帮助，既可用于诊断，也可用于预测。而要实现共性分析，我们需要注意从数据整理、数据连接，到算法选择、结果解析这些过程中，一环扣一环，不断尝试与验证。我们选择了JMP数据分析软件来助力实现这些。
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.