OPC Model Calibration via Density-Based Gauge Weighting
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Solution Overview
Problem
The accuracy of model-based optical proximity correction in integrated circuit manufacturing is often compromised due to trial-and-error or user-based weighting of OPC model gauges, leading to overfitting and inaccuracies in pattern fidelity during the photolithographic process.
Innovation Solution
A computing system implementing an optical proximity correction model calibration tool that automatically sets weights for OPC models based on gauge densities derived from feature vectors, using measurements and clustering algorithms to enhance the calibration process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If trial-and-error or user-based weighting methods are used for OPC model calibration, then the calibration process becomes flexible and adaptable to user preferences, but the accuracy and reliability of the optical proximity correction deteriorates due to overfitting and subjectivity
Solution Approach 1:
The system performs self-calibration by automatically determining optimal weights for OPC model gauges based on density calculations from layout data, eliminating the need for manual trial-and-error adjustment while improving accuracy through objective, data-driven weighting
Solution Approach 2:
The system uses measured pattern fidelity results to feed back into the weight determination process, where accuracy metrics inform the calibration algorithm to iteratively optimize gauge weights, creating a closed-loop system that continuously improves precision
2Ease of operation
If manual calibration methods are used, then the process allows for human judgment and adjustment, but the time consumption and complexity of the calibration process increases
Solution Approach 1:
The patent replaces manual human calibration operations with an automated computational system that calculates gauge weights algorithmically based on layout density metrics, substituting human judgment with machine-based objective functions to reduce time and complexity
Solution Approach 2:
The system dynamically adjusts calibration parameters (gauge weights) based on changing layout density conditions, automatically adapting the OPC model to different pattern densities without requiring manual recalibration for each scenario
3Device complexity
If uniform weighting is applied to all OPC model gauges, then the calibration process becomes simpler and more consistent, but the ability to handle varying pattern densities and complexities deteriorates
Solution Approach 1:
The patent applies different weights to different OPC model gauges based on their local importance and the specific pattern density they represent, allowing the calibration to adapt to varying complexities across different regions of the layout rather than applying a single uniform weight
Data Source
AI summary
This application discloses a computing system implementing an optical proximity correction model calibration tool to determine parameters for gauges describing features of an integrated circuit. The gauges include values corresponding to measurements collected for a set of the features. The optical proximity correction model calibration tool can ascertain densities of the gauges based on the measurements associated with the parameters for the gauges, and set weights for the gauges based, at least in part, on the densities. The optical proximity correction model calibration tool can calibrate an optical proximity correction (OPC) model using the weights for the gauges. The OPC model calibrated with the weights of the gauges can be utilized to predict of a printed image on a substrate described by a mask layout design corresponding to the integrated circuit.