Optical Proximity Correction (OPC) is an important step in the optical lithography-based manufacturing process. Starting from 115 nm, lithography processes typically use OPC to resolve features acceptably. Advanced OPC technologies use model-based edge segment adjustments to achieve highly accurate corrections. The typical process for optical proximity correction suffers from a huge turn-around-time (TAT) and is well known to have time-consuming complexity especially at 40 nm and below. Therefore, in order to speed up process development and increase qualified pattern variations with good yield, we must find ways to speed up the OPC TAT. This paper presents a flow to construct layout hierarchy and increase OPC cell/template re-use to greatly reduce the OPC TAT using the Pegasus Computational Pattern Analytics (CPA) software.
A multi-objective optimization flow is developed to identify balanced compact optical proximity correction (OPC) models with ideal calibration accuracy, runtime performance and prediction accuracy. We demonstrate a model selection process based on Pareto front optimization to meet multiple modeling requirements in a single optimization step. A genetic search algorithm determines the final population that offers the best trade-off in set model properties. As a demonstration, we cooptimize calibration accuracy, verification accuracy and term count in a mode developed for hot spot prediction for a line and space memory layer. The optimization determines the minimum number of model terms to meet the off-nominal dose and focus patterning accuracy requirements in verification. Multi-objective optimization provides better verification process window condition (PWC) accuracy because of the multi-objective trade-off built into the genetic algorithm (GA). The optimizer also provides better calibration accuracy (Rms Weighted) than compact models with a fixed configuration because model composition is optimized during GA search. The resulting champion model is 30% more predictive and 5% faster in simulation using this approach. Results for a negative tone develop hole layer with a model complexity of up to 44 terms are also analyzed based on nominal only measurement data. We further show the models selected by multi-objective optimization have a lesser tendency to over-fit the calibration data. The methodology can be applied to streamline complex models for optimum performance and target error rate. In many cases, for smaller data sets, we show that simplified models provide improved verification accuracy within metrology error limits.
Critical dimension analysis of cross-section image with delicate accuracy has become important demand for semiconductor manufacturing. In traditional analytic method, manual measurements always accompany large deviation and lower measured efficiency. Therefore, a robust and reliable analysis method is most essential objective to obtain accurate dimensions from PFA results. In this work, we demonstrate an intelligent image analysis method which is combined Mask Region based Convolution Neural Networks (Mask r-CNN) and image processing technique. Compared with manual measurement, intelligent image analysis method can achieve significant improvement on measured results in reproducibility, repeatability, and efficiency. This intelligent image analysis will provide novel applications in CD measurement, wafer defect analysis, and focus-exposure process window judgment.
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