The semiconductor industry faces a number of challenges that can be addressed using analytics and uncertainty quantification. Each generation of electronics and chip manufacturing demands greater levels of precision and consistency, effectively mandating an understanding of uncertainty’s effects on both the performance of the final product and the manufacturing process. Engineers must address unique challenges in maintaining this exacting quality, tracing complex root causes, and optimization and verification of manufacturing processes and equipment. As process monitoring techniques and data collection become more sophisticated and allow for increasing quantities of data to be captured, the need for a powerful, user-friendly, engineering analytics tool to analyze the data accurately and efficiently has never been greater.
SmartUQ provides cutting-edge technologies which can accelerate efforts to handle variation and uncertainty from all sources, help trace problems to their source, and improve process control through robust design. Understanding and reducing uncertainty can lead to improved product performance, faster design cycles, and lower costs.
SmartUQ worked with a Fortune 500 Semiconductor OEM that was using a highly detailed particle energy simulation as part of a semiconductor engineering process. The high-fidelity simulation was computationally intensive, limiting its utility, and the simulation response was a complex energy density function, making emulation difficult.
Using a combination of SmartUQ’s advanced continuous and functional emulation tools, the simulation response was emulated using a small number of simulation runs as the training sample. The emulator can accurately predict the relevant properties and shape of the particle energy density function with respect to the simulation input parameters.
The emulation process and tools demonstrated in this project allow the customer to more rapidly evaluate process changes, optimize designs, and investigate defect root causes much faster than before while using fewer computational resources.
A semiconductor manufacturing company was having difficulty with quality control and production optimization in a multistage batch plating process. SmartUQ took the manufacturer’s existing data, built processing scripts, and conducted both traditional analysis and advanced statistical modeling. These analyses teased out previously unknown correlations between multiple reactant concentrations, consumable status, and deposition rate. By understanding these relationships, previously unexplained process deviations were made clear and could be controlled, and further experiments for process optimization were identified.
Many useful insights into the nature of the manufacturing process and the relationships between inputs like consumables and output quality can be derived with statistical analysis once data sets are being collected and automatically processed into a suitable form. This type of analysis includes conducting sensitivity and variance analysis on existing manufacturing data to identify important inputs for use in process optimization and quality control.
In addition, advanced statistical model generation uses both existing data and intentional Designs of Experiment to train predictive models to mimic the behavior of the manufacturing process. This allows rapid prediction of the results from changes to processing steps, easier identification of root causes in failure analysis, and rapid process optimization.
SmartUQ has a complete suite of design of experiments, data sampling, emulation methods, and analytical tools to support the analytics needs of engineers in semiconductor industries. To learn more about analytics for semiconductor industries, check out SmartUQ white papers and webinars
