Machine Learning (ML) based technologies are actively being adopted in the computational lithography domain. ML-based methods have the potential to enhance the accuracy of predictive models, speed up the run-times of the mask optimization processes and produce consistent results compared with the other numerical methods. In this paper, we present the result of an ML-based ILT application to an advanced DRAM contact layer for both core and periphery region. In our ML ILT method, golden mask layouts are generated by ProteusTM ILT tool for the sampled target layouts to obtain reliable training inputs, which are then used to train a custom-designed Convolutional Neural Network (CNN). The trained CNN is plugged-in to the conventional ILT flow as an initial mask provider and the entire
As design rules of advance devices shrink down, not only process-window budget of lithography process is getting tighter, but also CD control to target is more important especially for multiple tool process environment in HVM (High Volume Manufacturing). The tool induced CD bias or CD difference between tools are derived by minute amount of residual imaging parameters even though with strict control in system. The tool to tool CD mismatch is able to be reduced to nanometer or sub-nanometer scale for critical features of concern by using released tools such as LithoTuner PMFCTM (Pattern Matcher Full Chip). During the matching process, tunable imaging parameters such as pupil shape and stage tilt can be used as matching knobs. In this paper, CD mismatch due to film stack change on same exposure tool was studied to check feasibility of PMFC application. Also, CD variation and its impact on CD mismatch by focus error as amount of intrinsic system was investigated as well. By considering the focus impact on CD proximity bias via simple mathematical ways, the CD matching process could be more accurately performed and verified.
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