Data Infrastructure Bottlenecks Hinder Advanced Semiconductor Testing and ML Adoption
The semiconductor industry's rapid adoption of machine learning for advanced testing, including adaptive binning and real-time analytics, is being hindered by inadequate data infrastructure. According to experts Greg Prewitt and Marc Jacobs, the primary challenge is not computational power or algorithms, but rather the quality, completeness, and correct association of data flowing from fabrication to testing. Poor metadata correlation often undermines automated processes, as computers cannot intuitively correct disjointed data like human analysts can. Consequently, data correlation failures are becoming a significant operational risk, prompting leading manufacturers to treat data quality as a formal key performance indicator. The article outlines two foundational requirements for robust data infrastructure: direct data collection at the measurement tool to avoid omissions common with outsourced assembly and test providers, and continuous cross-referencing against a system of record, such as a manufacturing execution system. This ensures data integrity and enables effective feed-forward models. Without these improvements, the industry's aspirations for sophisticated, model-driven testing will remain out of reach due to foundational data plumbing issues.
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Data Infrastructure Bottlenecks Hinder Advanced Semiconductor Testing and ML Adoption
The semiconductor industry's rapid adoption of machine learning for advanced testing, including adaptive binning and real-time analytics, is being hindered by inadequate data infrastructure. According to experts Greg Prewitt and Marc Jacobs, the primary challenge is not computational power or algorithms, but rather the quality, completeness, and correct association of data flowing from fabrication to testing. Poor metadata correlation often undermines automated processes, as computers cannot intuitively correct disjointed data like human analysts can. Consequently, data correlation failures are becoming a significant operational risk, prompting leading manufacturers to treat data quality as a formal key performance indicator. The article outlines two foundational requirements for robust data infrastructure: direct data collection at the measurement tool to avoid omissions common with outsourced assembly and test providers, and continuous cross-referencing against a system of record, such as a manufacturing execution system. This ensures data integrity and enables effective feed-forward models. Without these improvements, the industry's aspirations for sophisticated, model-driven testing will remain out of reach due to foundational data plumbing issues.
Semiconductor Engineering