Using machine learning etch models in OPC and ILT correction
As feature resolution and process variations continue to shrink for new nodes of both DUV and EUV lithography, the density and number of devices on advanced semiconductor masks continue to increase rapidly. These advances cause significantly increased pressure on the accuracy and efficiency of OPC mask output. Specifically, accurately considering etch effects within OPC is becoming more critical. Mask synthesis flows have typically accounted for etch proximity effects using rule based approaches, and the accuracy limitations of fast etch models has limited wide spread adoption of model based etch mask correction approaches. Several publications and industry presentations have discussed the use of neural networks or other machine learning techniques to provide improvements in both accuracy and efficiency in mask synthesis flows. In this paper, we present results of using machine learning in etch models to improve model accuracy without sacrificing TAT. Then we demonstrate usage of the machine learning etch model in both ILT and OPC to highlight the benefits of improved accuracy of etch compensation.