Data analytics for time constraint adherence prediction in a semiconductor manufacturing use-case

نویسندگان

چکیده

Semiconductor manufacturing represents a challenging industrial environments, where products require more than several hundred operations, each representing the technical state-of-the-art. Products vary greatly in volume, design and required production processes and, additionally, product portfolios technologies change rapidly. Thus, technologically restricted rapid development, stringent quality related clean room requirements high precision equipment application enforce operational excellence, particular time constraints adherence. Product specific between two or successive process operations are an industry-specific challenge, as violations lead to additional scrapping reworking costs. Time constraint adherence is linked dispatching currently manually assessed. To overcome this error-prone manual task, article presents data-based decision predict semiconductor manufacturing. Real-world historical data analyzed appropriate statistical models scoring functions derived. Compared other relevant literature regarding violations, central contribution of design, generation validation model for quality-related based on real-world plant.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing

Smart manufacturing (SM) is a term generally applied to the improvement in manufacturing operations through integration of systems, linking of physical and cyber capabilities, and taking advantage of information including leveraging the big data evolution. SM adoption has been occurring unevenly across industries, thus there is an opportunity to look to other industries to determine solution an...

متن کامل

Cycle Time Prediction for Semiconductor Manufacturing via Simulation on Demand

Traditionally, competition between semiconductor manufacturers has primarily focused on product design and cost. Recently, speed of delivery has also become an important differentiator among these firms which has led to manufacturing cycle time becoming a critical performance measure. This paper presents a methodology that performs a limited set of simulation runs for a complex wafer fabricatio...

متن کامل

A Fuzzy TOPSIS Approach for Big Data Analytics Platform Selection

Big data sizes are constantly increasing. Big data analytics is where advanced analytic techniques are applied on big data sets. Analytics based on large data samples reveals and leverages business change. The popularity of big data analytics platforms, which are often available as open-source, has not remained unnoticed by big companies. Google uses MapReduce for PageRank and inverted indexes....

متن کامل

A data mining approach to employee turnover prediction (case study: Arak automotive parts manufacturing)

Training and adaption of employees are time and money consuming. Employees’ turnover can be predicted by their organizational and personal historical data in order to reduce probable loss of organizations. Prediction methods are highly related to human resource management to obtain patterns by historical data. This article implements knowledge discovery steps on real data of a manufacturing pla...

متن کامل

Combining SOM and GA-CBR for Flow Time Prediction in Semiconductor Manufacturing Factory

Flow time of semiconductor manufacturing factory is highly related to the shop floor status; however, the processes are highly complicated and involve more than hundred of production steps. Therefore, a simulation model with the production process of a real wafer fab located in Hsin-Chu Sciencebased Park of Taiwan is built. In this research, a hybrid approach by combining Self-Organizing Map (S...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Procedia CIRP

سال: 2021

ISSN: ['2212-8271']

DOI: https://doi.org/10.1016/j.procir.2021.05.008