Optical Proximity Correction Using Precomputed Process Window Lookup Tables
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Solution Overview
Problem
Current optical proximity correction (OPC) methods are computationally inefficient and require repeated simulations at various process conditions to account for process window variations, leading to prohibitive computational time and challenges in validating and designing target circuits.
Innovation Solution
A computationally efficient OPC method that determines a function to generate simulated images, optimizing target gray levels based on process variations, using polynomial functions and Gaussian probability distributions to maximize the process window, reducing computation time by approximately half compared to prior art methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If repeated simulations at various process conditions are performed to account for process window variations, then the accuracy of OPC results is improved, but the computational time becomes prohibitive
Solution Approach 1:
The patent pre-calculates process window parameters and stores them in lookup tables before the actual OPC simulation. This preliminary action allows the OPC process to directly retrieve pre-computed values instead of performing repeated simulations at various process conditions, significantly reducing computational time while maintaining accuracy
Solution Approach 2:
The patent creates simplified models or approximations of the complex lithography process by developing lookup tables that capture the essential behavior of process window variations. These copied representations allow fast querying during OPC without requiring full repeated simulations
2Manufacturing precision
If process window maximization is implemented in OPC, then the manufacturing precision is improved, but the device complexity increases
Solution Approach 1:
The patent segments the complex process window maximization problem into distinct components: pre-computation of process parameters, creation of lookup tables, and integration into OPC. This segmentation allows each component to be optimized independently and simplifies the overall implementation
Solution Approach 2:
The patent transforms the complex process window maximization problem into a set of manageable parameter variations that can be pre-characterized and stored. By changing the approach from direct optimization to parameter-based lookup, the complexity is reduced while maintaining the ability to maximize process window
Data Source
AI summary
The present invention relates to an efficient OPC method of increasing imaging performance of a lithographic process utilized to image a target design having a plurality of features. The method includes the steps of determining a function for generating a simulated image, where the function accounts for process variations associated with the lithographic process; and optimizing target gray level for each evaluation point in each OPC iteration based on this function. In one given embodiment, the function is approximated as a polynomial function of focus and exposure, R(ε, f )=P0+f2·Pb with a threshold of T+Vε for contours, where P0 represents image intensity at nominal focus, f represents the defocus value relative to the nominal focus, ε represents the exposure change, V represents the scaling of exposure change, and parameter “Pb” represents second order derivative images. In another given embodiment, the analytical optimal gray level is given for best focus with the assumption that the probability distribution of focus and exposure variation is Gaussian.