Centralized Semiconductor Data Structure for Outlier Detection
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Solution Overview
Problem
Current methods for semiconductor manufacturing lack effective data sharing and analysis across the electronic supply chain, leading to inefficiencies in product quality, yield, and testing processes.
Innovation Solution
A centralized data structure is established to interconnect sources and users of semiconductor and electronic device manufacturing data, enabling data sharing, analytics, and action execution across multiple supply chain stages, including IC manufacturing, module assembly, and system assembly, with features like outlier identification and data anonymization for secure data protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is shared across the electronic supply chain, then product quality and testing efficiency are improved, but data security and proprietary information protection deteriorate
Solution Approach 1:
A centralized data structure acts as an intermediary hub between component manufacturers and system manufacturers. This mediator enables data sharing and analytics while implementing security measures such as data scrubbing and anonymization to protect proprietary information, thus resolving the contradiction between quality improvement and security protection
2Loss of information
If centralized data collection is implemented, then analytics capability and traceability are improved, but system complexity increases
Solution Approach 1:
The centralized data structure is designed to perform multiple functions including data collection, storage, analytics, outlier identification, and security protection within a single system. This multi-functional approach enables comprehensive traceability and analytics capability while managing system complexity through integration rather than multiple separate systems
3Manufacturing precision
If outlier detection is performed across all components, then manufacturing precision is improved, but processing time and computational resources increase
Solution Approach 1:
The system extracts and identifies only outlier components that deviate from normal patterns rather than processing all component data uniformly. This selective approach focuses computational resources on identifying quality issues while reducing overall processing time and computational burden
Data Source
AI summary
A method includes receiving system test data for a plurality of electronic systems. Each of the electronic systems includes a plurality of electronic components. The method also includes determining a relationship between a set of electronic components and the electronic systems upon which the electronic components of the set of electronic components are assembled and receiving manufacturing attributes including spatial data for the set of electronic components. The method further includes selecting a data subset from the system test data corresponding to a subgroup of the set of electronic components. The subgroup includes components within an area defined on a substrate according to a spatial pattern and that is fewer than all of the set of electronic components on the substrate. Additionally, the method includes identifying an outlier relative to the data subset and communicating information about the outlier to at least one of a system or a component manufacturer.