Shape Simulator Parameter Tuning for Semiconductor Process Prediction
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Solution Overview
Problem
Existing information processing systems for optimizing process conditions in semiconductor manufacturing struggle to accurately predict the shape changes of workpieces under varying process conditions, leading to suboptimal process outcomes.
Innovation Solution
An information processing apparatus that generates simulation data comprising combinations of unprocessed and processed data for workpieces under different process conditions, using a shape simulator to derive optimal simulation parameters based on closeness metrics, and a process condition optimization apparatus that determines precise process settings to achieve target end states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulation data includes only limited combinations of unprocessed and processed data, then the complexity of data processing is reduced, but the prediction accuracy of workpiece shape changes deteriorates
Solution Approach 1:
The patent segments the simulation data into multiple distinct combinations, where each combination contains paired unprocessed and processed data for specific process conditions. This segmentation allows the system to handle complex prediction tasks by breaking them down into manageable data units, improving prediction accuracy without overwhelming the processing system with undifferentiated data mass.
Solution Approach 2:
The patent applies local quality by ensuring that each data combination contains specific, targeted information relevant to particular process conditions (such as etching conditions, deposition conditions, or ion implantation conditions). This localized data structure enables the shape simulator to accurately predict shape changes for specific scenarios while maintaining overall system efficiency.
2Measurement precision
If multiple combinations of unprocessed and processed data are used, then the prediction accuracy of workpiece shape changes improves, but the time required for processing increases
Solution Approach 1:
The patent implements preliminary action by pre-organizing simulation data into multiple ready-to-use combinations before actual prediction tasks. Each combination is structured with corresponding unprocessed and processed data, allowing the shape simulator to quickly retrieve and apply appropriate data sets without performing time-consuming data preparation during the prediction phase.
Solution Approach 2:
The patent uses copying by creating multiple replicated data combinations that represent different process conditions. These copied data sets can be efficiently stored and retrieved, enabling parallel processing and reducing the time required to analyze complex shape changes under varying conditions.
3Reliability
If simulation parameters are derived using closeness metrics between predicted and actual processed data, then the reliability of shape simulation improves, but the computational complexity increases
Solution Approach 1:
The patent implements feedback by using closeness metrics to compare predicted processed data with actual processed data, then deriving simulation parameters that minimize the difference between them. This feedback loop continuously refines the simulation model, improving reliability by ensuring the shape simulator accurately reflects real-world process outcomes while maintaining computational efficiency through iterative parameter optimization.
Data Source
AI summary
An information processing apparatus includes a generation unit that generates simulation data including a plurality of combinations of unprocessed data of a workpiece and processed data of the workpiece after a process is performed on the workpiece under a predetermined process condition. Each of the plurality of combinations includes the unprocessed data and the processed data when the process is performed with a plurality of pattern densities for each of a plurality of mask shapes. The information processing apparatus further includes a derivation unit that derives simulation parameters of a shape simulator based on a closeness between predicted data that is predicted by inputting the unprocessed data included in the simulation data to the shape simulator, and the processed data combined with the unprocessed data.