Semiconductor Contact Structure Etching With ML Parameter Feedback
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Solution Overview
Problem
Current etching processes in semiconductor fabrication face challenges in consistently forming thin films with precise thickness and composition, leading to performance issues in integrated circuits and increased wafer scrap rates due to variability in process conditions.
Innovation Solution
The implementation of machine learning techniques to train an analysis model that dynamically adjusts thin-film etching process parameters, ensuring that the etching processes produce films with target specifications by controlling factors such as fluid flow, pressure, and temperature, thereby improving the reliability of thin-film formation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional etching processes are used to form thin films, then manufacturing complexity is reduced, but manufacturing precision deteriorates due to variability in film thickness and composition
Solution Approach 1:
The patent implements dynamic adjustment of etching process parameters (gas flow rates, pressure, power, temperature) during the etching process based on real-time monitoring of thin film thickness and composition. This allows the system to adapt parameters to achieve target specifications while managing process complexity through automated control.
Solution Approach 2:
The system incorporates real-time monitoring and feedback mechanisms that measure thin film properties during etching and automatically adjust process parameters to maintain precision. The feedback loop ensures that variations in film thickness and composition are corrected dynamically, resolving the contradiction between precision and complexity.
2Reliability
If etching process conditions are relaxed for easier operation, then ease of operation improves, but reliability deteriorates due to inconsistent thin film formation
Solution Approach 1:
The etching system performs self-adjustment through automated control algorithms that monitor thin film formation in real-time and automatically modify process parameters without operator intervention. This self-service capability ensures reliable thin film formation while simplifying operation, as the system handles its own optimization.
Solution Approach 2:
The patent replaces manual mechanical control of etching parameters with automated electronic control systems and algorithms. This substitution enables precise, reliable thin film formation through computerized parameter management, improving reliability while reducing the complexity of manual operation and monitoring.
Data Source
AI summary
A semiconductor process system etches thin films on semiconductor wafers. The semiconductor process system includes a machine learning based analysis model. The analysis model dynamically selects process conditions for an etching process by receiving static process conditions and target thin-film data. The analysis model identifies dynamic process conditions data that, together with the static process conditions data, result in predicted remaining thin-film data that matches the target thin-film data. The process system then uses the static and dynamic process conditions data for the next etching process.