AI Process Planning for High-Yield Component Carrier Manufacturing
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Solution Overview
Problem
The manufacturing of component carriers faces challenges in achieving high yield and efficiency due to undesired properties in the final products, which are often caused by the complexity of process parameters and the increasing miniaturization of electronic components, leading to issues such as heat removal, mechanical robustness, and electrical reliability, especially under harsh conditions.
Innovation Solution
A method and apparatus utilizing an artificial intelligence module to determine an action plan for manufacturing component carriers by ranking and selecting key process parameters, which are then used to adjust manufacturing settings, ensuring compliance with defined final product parameters through AI-based predictive modeling and deep learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional engineering planning is used for manufacturing component carriers, then the manufacturing process can be carried out with standard procedures, but yield loss occurs due to undesired properties in final products
Solution Approach 1:
The patent applies parameter changes by using AI/ML algorithms to optimize process parameters based on historical data and patterns. The system dynamically adjusts manufacturing parameters to achieve desired final product properties, thereby improving yield while maintaining quality. This is evident in the use of machine learning models that predict optimal parameter settings to prevent defects before they occur.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring manufacturing processes and using AI algorithms to analyze outcomes. The system learns from past manufacturing results and adjusts process parameters in real-time or near-real-time to prevent recurrence of defects. This closed-loop approach improves both yield and product reliability by enabling continuous process optimization.
2Manufacturing precision
If all process parameters are considered in manufacturing planning, then comprehensive control is achieved, but computational burden increases significantly
Solution Approach 1:
The patent extracts and focuses on the most critical process parameters that have the greatest impact on final product properties. The AI/ML system identifies and prioritizes key parameters from the full set of process parameters, analyzing only the most influential ones. This extraction approach maintains manufacturing precision while significantly reducing computational burden by eliminating analysis of less significant parameters.
Solution Approach 2:
The patent segments the manufacturing process into distinct stages and analyzes parameters at each stage separately using hierarchical AI models. This segmentation allows the system to handle complex manufacturing processes in manageable chunks, improving precision through stage-specific optimization while reducing overall computational complexity through modular analysis.
3Adaptability or versatility
If miniaturization of electronic components is pursued to increase functionality, then product capabilities improve, but heat removal and mechanical robustness become increasingly difficult
Solution Approach 1:
The patent applies local quality by optimizing manufacturing parameters specifically for miniaturized components with different thermal and mechanical requirements. The AI system identifies regions with different heat dissipation needs and applies localized process parameter adjustments. This enables the manufacturing of highly functional miniaturized components while maintaining adequate thermal and mechanical performance through region-specific optimization.
Solution Approach 2:
The patent implements preliminary action by using AI/ML models to predict and prevent thermal and mechanical issues before they occur during component operation. The system analyzes design and manufacturing parameters in advance to identify potential heat removal or robustness problems, adjusting manufacturing parameters proactively to ensure miniaturized components meet thermal and mechanical requirements before production.
Data Source
AI summary
A method of planning the manufacture of component carriers includes defining a set of final product parameters as a target for component carriers to be manufactured, ranking the process parameters concerning their impact on the final product parameters, selecting a subset of higher ranked process parameters, inputting the selected subset of process parameters for processing by an artificial intelligence module, and determining an action plan for the manufacturing based on an output of the artificial intelligence module, where the product parameters are influenceable by a set of process parameters settable during the manufacturing method.