Process Model Quality Assessment via Gradient Convolution
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Solution Overview
Problem
Current methods for assessing the quality of process models in semiconductor manufacturing are labor-intensive, time-consuming, and limited by small sample sizes, making it difficult to guarantee predictive accuracy and distinguish between data collection errors and model errors.
Innovation Solution
A system that computes a quality indicator by convolving the gradient of a process model with a mask layout, allowing for rapid assessment of process model quality by comparing the indicator to thresholds, and refitting the model if necessary to improve its accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If measurement-based techniques are used to assess process model quality, then the assessment can be performed with empirical data, but the processing and data collection time becomes excessively long and labor-intensive
Solution Approach 1:
The patent creates a virtual copy of the manufacturing process through simulation. Instead of physically measuring actual process outcomes (which is time-consuming), the system uses a process model to simulate and predict process results. This virtual copying allows rapid assessment of process model quality without the delays of physical data collection and processing.
Solution Approach 2:
The patent replaces the mechanical/physical measurement system with a computational simulation system. Rather than physically fabricating test patterns and measuring their outcomes (mechanical process), the system uses computational models to simulate the manufacturing process and assess model quality, dramatically reducing time requirements.
2Productivity
If a small number of test patterns are used for model assessment, then the data collection process is faster, but the accuracy of the assessment is severely limited
Solution Approach 1:
The patent develops a universal assessment methodology that can evaluate process model quality across diverse pattern configurations simultaneously. The simulation-based approach creates a multi-functional assessment system that can handle various pattern types and process conditions within a single framework, allowing comprehensive evaluation without being limited by small sample sizes of specific test patterns.
Solution Approach 2:
The patent changes the assessment parameters from physical measurements of actual manufactured patterns to computational metrics derived from simulation data. By transforming the assessment into a parameter-based computational analysis rather than physical measurement, the system achieves both high speed and high accuracy simultaneously.
3Reliability
If measurement-based techniques are used to assess process model quality, then empirical validation is possible, but it becomes difficult to determine whether inaccuracies result from data collection errors or model errors
Solution Approach 1:
The patent extracts and isolates the process model from the complex measurement and data collection process. By using simulation to assess the model independently of physical manufacturing variations and measurement errors, the system cleanly separates model performance evaluation from data collection quality issues, making error source identification straightforward.
Solution Approach 2:
The patent introduces simulation as an intermediary between the process model and empirical validation. This intermediary layer allows the model to be tested and assessed in a controlled computational environment before physical implementation, providing clear insight into model performance independent of data collection errors or manufacturing variations.
Data Source
AI summary
One embodiment of the present invention provides a system that assesses the quality of a process model. During operation, the system receives a mask layout and additionally receives a process model that models the effects of one or more semiconductor manufacturing processes on the mask layout. Next, the system computes a gradient of the process model with respect to a process model parameter. The system then computes a quality indicator at an evaluation point in the mask layout using the gradient of the process model and the mask layout. Next, the system assesses the quality of the process model using the quality indicator. In one embodiment, the system assesses the quality of the process model by comparing the quality indicator with a threshold.