Optical Proximity Correction Model Accuracy via Parameter Integration
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Solution Overview
Problem
Current optical proximity correction (OPC) models in semiconductor manufacturing are incomplete, leading to increased manufacturing costs, longer time to market, and decreased quality due to their inability to accurately account for optical proximity effects, which are exacerbated by the challenges of producing finer circuit patterns in advanced technologies like 45 nm technologies.
Innovation Solution
A modeling technique that incorporates additional parameters such as illuminator details, lens signatures, flare data, chromatic aberrations, and synchronization errors into OPC models to generate a simulated, corrected reticle design, improving the accuracy and efficiency of the OPC process by comparing model exposure differences against predetermined criteria and iteratively adjusting parameters until the desired accuracy is achieved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OPC models are used, then the OPC process can be performed with basic parameters, but the accuracy of OPE correction deteriorates due to incomplete modeling factors
Solution Approach 1:
The patent applies parameter changes by expanding the OPC model to include additional physical parameters such as illuminator details, lens signatures, flare data, chromatic aberrations, and synchronization errors. This transforms the model from using basic parameters to using comprehensive parameters that capture all significant optical effects, thereby improving measurement precision without excessively increasing device complexity through systematic parameter integration
Solution Approach 2:
The patent introduces an intermediary computational framework that systematically integrates multiple optical parameters (illuminator, lens, flare, chromatic aberration, synchronization) into a unified OPC model. This intermediary model acts as a bridge between individual optical components and the final pattern correction, allowing accurate OPE prediction while managing complexity through structured parameter combination
2Measurement precision
If iterative manual adjustment of OPC models is performed, then model accuracy can be improved through best guesses and estimations, but the time required for OPC model development increases significantly
Solution Approach 1:
The patent implements feedback mechanisms where the OPC model is validated against actual exposure data, and model parameters are automatically adjusted based on the difference between predicted and measured results. This closed-loop feedback system replaces manual best-guess adjustments with systematic, data-driven parameter optimization, improving accuracy while reducing the time required for model development
Solution Approach 2:
The patent enables the OPC model to self-adjust parameters automatically through computational algorithms that optimize model fit to exposure data without requiring continuous manual intervention. The system performs self-service by automatically selecting and tuning parameters such as illuminator details, lens signatures, and chromatic aberrations based on measured performance, eliminating the need for lengthy manual iteration processes
3Measurement precision
If comprehensive optical parameters are included in the model, then OPE prediction accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies segmentation by dividing the comprehensive optical model into distinct modular components: illuminator module, lens module, flare module, chromatic aberration module, and synchronization module. Each module processes specific parameters independently, allowing accurate OPE prediction through coordinated computation while maintaining productivity by avoiding the need to process all parameters simultaneously in a monolithic computation
4Ease of manufacture
If manual best guesses and estimations are used for OPC model compensation, then the process can proceed with available information, but errors and omissions increase leading to decreased manufacturing quality
Solution Approach 1:
The patent replaces the manual mechanical process of best-guess parameter adjustment with an automated computational system. Instead of engineers manually estimating and adjusting OPC parameters, the system uses computational algorithms to automatically determine optimal parameter values based on exposure data, eliminating human error and omission while maintaining process feasibility
Solution Approach 2:
The patent introduces an intermediary computational validation layer that systematically verifies model parameters against actual exposure results. This intermediary process detects and corrects errors and omissions that would otherwise propagate through the manufacturing process, improving reliability while keeping the overall process automated and feasible without requiring exhaustive manual checking
Data Source
AI summary
A modeling technique is provided. The modeling technique includes inputting tool parameters into a model and inputting basic model parameters into the model. The technique further includes generating a simulated, corrected reticle design using the tool parameters and the basic model parameters. An image of test patterns is compared against the simulated, corrected reticle design. A determination is made as to whether δ1<ε1, wherein δ1 represents model vs. exposure difference and ε1 represents predetermined criteria. The technique further includes completing the model when δ1<ε1.