Getech Becomes a Member of SEMI China Smart Manufacturing Committee
On November 19, the thematic forum ["Intelligent" Manufacturing Future—Advanced Semiconductor Manufacturing] of the 2020 Beijing Microelectronics International Symposium and IC WORLD Academic Conference was successfully held, which was also one of the meetings of the SEMI China Smart Manufacturing Committee.
Dr. Wang Jin, Chief Smart Manufacturing Consultant of Getech, was invited to attend and delivered a keynote speech titled "Application of Big Data Analytics in the Semiconductor Manufacturing Industry," sharing the role and value of virtual metrology, business outcomes, and its application and popularization in the semiconductor manufacturing field.
Dr. Wang Jin believes that due to the high precision requirements of semiconductor manufacturing, its preparation process faces challenges such as repeated re-entry, long production cycles, numerous process steps, multiple varieties and batches, and short waiting times between processes. Additionally, the semiconductor manufacturing process generates vast amounts of data, and if this data cannot be utilized effectively, it poses significant risks.
To achieve intelligence in semiconductor manufacturing data, it is essential to enhance data completeness, mechanism certainty, and business timeliness, thereby improving production effectiveness.
1. Data Completeness: Getech's Industrial Application Intelligent Platform has robust data collection and processing capabilities, enabling the integration of isolated information systems, consolidating multi-source data, supporting production line adjustments and innovations, and enhancing equipment data completeness.
2. Mechanism Certainty: Mechanism certainty can be divided into three stages from low to high: manual search -> automatic monitoring -> intelligent analysis.
Manual Search Stage: Equipment and process engineers rely on work experience to identify key factors, which is inefficient and time-consuming. Automatic Monitoring Stage: Single-factor modeling systems monitor the status of individual factors in real-time and intercept anomalies. However, due to the single dimension of monitoring, the effectiveness of the monitoring model remains questionable. Intelligent Analysis Stage: Multi-factor analysis modeling tools analyze the interactive effects of multiple factors, quickly identify abnormal factors, determine root causes, and implement corrective and preventive measures.
Getech's MFA multi-factor modeling tool can identify key factors affecting production and product anomalies in real-time, reduce production losses, and enhance equipment mechanism certainty.
3. Business Timeliness: Getech's virtual metrology tool can effectively improve business timeliness.
Virtual Metrology (VM) is a method that does not require physical measurement operations but instead uses data collected from process tools, applies intelligent algorithms, and predicts process quality in real-time. VM uses pre-processed measurable data and production measurable data output by systems such as FDC as inputs to predict batch-end data characteristics. It is a potential solution for providing low-cost, fast, and accurate measurement information, thereby improving equipment business timeliness.
Process parameters and actual measurement data undergo four stages: data preprocessing, data filtering, model establishment, and model self-optimization, to output predicted quantities that are difficult to measure.
By applying virtual metrology, comprehensive quality prediction can be achieved, sampling frequency can be reduced, production cycles can be shortened, the number of measurement devices purchased can be decreased, and costs can be lowered. It also has significant application prospects in the semiconductor manufacturing field. Issues such as uneven wafer edge film thickness in semiconductor manufacturing and various defects in CMP wafers can be addressed through MFA multi-factor analysis and virtual metrology model construction. This shifts wafer manufacturing processes from experience-based decision-making to data-driven decision-making, significantly improving CMP process yields, enhancing the stability of product electrical parameters, reducing product sampling rates, and shortening production cycles.
At the closed-door meeting of the SEMI China Smart Manufacturing Committee held that afternoon, Ju Long, Global Vice President and President of SEMI China, presented Getech with the membership unit plaque.
After joining the SEMI China Smart Manufacturing Committee, Getech will continue to advance its efforts in assisting semiconductor companies with intelligent transformation, contributing to the rapid development of China's smart manufacturing technology, and laying a solid foundation.
Dr. Wang Jin, Chief Smart Manufacturing Consultant of Getech, was invited to attend and delivered a keynote speech titled "Application of Big Data Analytics in the Semiconductor Manufacturing Industry," sharing the role and value of virtual metrology, business outcomes, and its application and popularization in the semiconductor manufacturing field.
Dr. Wang Jin believes that due to the high precision requirements of semiconductor manufacturing, its preparation process faces challenges such as repeated re-entry, long production cycles, numerous process steps, multiple varieties and batches, and short waiting times between processes. Additionally, the semiconductor manufacturing process generates vast amounts of data, and if this data cannot be utilized effectively, it poses significant risks.
To achieve intelligence in semiconductor manufacturing data, it is essential to enhance data completeness, mechanism certainty, and business timeliness, thereby improving production effectiveness.
1. Data Completeness: Getech's Industrial Application Intelligent Platform has robust data collection and processing capabilities, enabling the integration of isolated information systems, consolidating multi-source data, supporting production line adjustments and innovations, and enhancing equipment data completeness.
2. Mechanism Certainty: Mechanism certainty can be divided into three stages from low to high: manual search -> automatic monitoring -> intelligent analysis.
Manual Search Stage: Equipment and process engineers rely on work experience to identify key factors, which is inefficient and time-consuming. Automatic Monitoring Stage: Single-factor modeling systems monitor the status of individual factors in real-time and intercept anomalies. However, due to the single dimension of monitoring, the effectiveness of the monitoring model remains questionable. Intelligent Analysis Stage: Multi-factor analysis modeling tools analyze the interactive effects of multiple factors, quickly identify abnormal factors, determine root causes, and implement corrective and preventive measures.
Getech's MFA multi-factor modeling tool can identify key factors affecting production and product anomalies in real-time, reduce production losses, and enhance equipment mechanism certainty.
3. Business Timeliness: Getech's virtual metrology tool can effectively improve business timeliness.
Virtual Metrology (VM) is a method that does not require physical measurement operations but instead uses data collected from process tools, applies intelligent algorithms, and predicts process quality in real-time. VM uses pre-processed measurable data and production measurable data output by systems such as FDC as inputs to predict batch-end data characteristics. It is a potential solution for providing low-cost, fast, and accurate measurement information, thereby improving equipment business timeliness.
Process parameters and actual measurement data undergo four stages: data preprocessing, data filtering, model establishment, and model self-optimization, to output predicted quantities that are difficult to measure.
By applying virtual metrology, comprehensive quality prediction can be achieved, sampling frequency can be reduced, production cycles can be shortened, the number of measurement devices purchased can be decreased, and costs can be lowered. It also has significant application prospects in the semiconductor manufacturing field. Issues such as uneven wafer edge film thickness in semiconductor manufacturing and various defects in CMP wafers can be addressed through MFA multi-factor analysis and virtual metrology model construction. This shifts wafer manufacturing processes from experience-based decision-making to data-driven decision-making, significantly improving CMP process yields, enhancing the stability of product electrical parameters, reducing product sampling rates, and shortening production cycles.
At the closed-door meeting of the SEMI China Smart Manufacturing Committee held that afternoon, Ju Long, Global Vice President and President of SEMI China, presented Getech with the membership unit plaque.
After joining the SEMI China Smart Manufacturing Committee, Getech will continue to advance its efforts in assisting semiconductor companies with intelligent transformation, contributing to the rapid development of China's smart manufacturing technology, and laying a solid foundation.
