OPC Model Verification via Pattern Classification
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Solution Overview
Problem
The accuracy of optical proximity correction (OPC) models in integrated circuit manufacturing is often inadequate, leading to errors in mask layout design data and potential faults in manufactured integrated circuits, especially as feature sizes decrease and components become more densely packed, resulting in diffractive effects that hinder faithful image reproduction during photolithographic processes.
Innovation Solution
A computing system is implemented to verify OPC models by determining parameters for design patterns in a layer file, comparing them to calibration patterns, and classifying differences to identify potential errors or lithographic difficulties, allowing for re-calibration or corrective actions such as re-performing the OPC process or analyzing taped-out masks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If feature size is reduced and components are densely packed to increase circuit capacity, then the number of circuit components per area increases, but diffractive effects during photolithography worsen and image reproduction accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by performing optical proximity correction on the mask layout design data before the photolithographic process. The system predicts diffractive effects and adjusts the mask pattern in advance to compensate for expected printing errors, thereby improving image reproduction accuracy while maintaining high circuit component density
Solution Approach 2:
The patent utilizes parameter changes by modifying the mask layout design parameters (such as edge positions, widths, and shapes) based on predicted diffractive effects. The system adjusts these geometric parameters to counteract the worsening diffractive effects caused by reduced feature sizes, thereby maintaining manufacturing precision
2Measurement precision
If OPC model accuracy is improved by using complex models and extensive calibration, then pattern prediction accuracy increases, but computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by implementing a multi-level verification process that checks only critical patterns with high lithographic difficulty first. The system performs detailed OPC model verification on selected critical patterns rather than all patterns, thereby achieving sufficient accuracy while reducing computational complexity
Solution Approach 2:
The patent utilizes segmentation by dividing the verification process into multiple stages: initial rapid verification, detailed verification of critical patterns, and final validation. This segmented approach allows the system to achieve high OPC model accuracy for critical patterns while managing overall computational complexity through prioritized processing
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of OPC models by classifying design patterns and modifying the layer file to address errors, thereby improving pattern fidelity and reducing manufacturing flaws in integrated circuits.
Implementation Method 1
The diffractive effects of radition often result in defects where the intended image is not accurately printed onto the substrate during the photolithographic process
Data Source
AI summary
A computing system implementing an optical proximity correction model verification tool can determine parameters for design patterns associated with an integrated circuit described in a layer file, and determine differences between the design patterns and calibration patterns utilized to calibrate an optical proximity correction (OPC) model configured to predict a printed image on a substrate corresponding to a layout design for the integrated circuit by determining distances between the determined parameters for the design patterns and parameters for the calibration patterns. The computing system can classify the design patterns with a modeling capability of the OPC model for the design patterns based on the differences between design patterns and the calibration patterns and possibly error rates of the OPC model associated with the calibration patterns or lithographic difficulty of the calibration patterns. The computing system can modify the layer file to include the classifications of the design patterns.