SEM Image Transformation Between Litho Domain and Etch Domain

In semiconductor manufacturing, a forward etching process model that can accurately predict the pattern transformation from post-lithography (ADI) to post-etch (AEI) is greatly desired. However, current etching model is etch bias based, it is unable to offer rich information as the SEM image does for engineers to do wafer disposition. In practice, ambiguous circumstances often arise when a disposition decision cannot be made easily by examining the post-lithography pattern image alone. The probability of making the disposition decision correctly can be greatly enhanced if the post etch image can be predicted accurately. Likewise, an inverse etching model that can predict post lithography pattern SEM image from post-etch pattern SEM image is also desired for OPC lithography target layer definition. Current OPC lithography target layer is derived from etch bias estimation from a forward etching model. The rigorous solution to OPC lithography target layer generation should come from an inverse etching model instead of the forward etching model. These practical needs have motivated us to develop models that can transform SEM images between post lithography domain and post etch domain freely and accurately. In this paper, we will report results from our proposed image based forward etching model and the image based inverse etching model. The deep convolutional neural network (DCNN) models have achieved SEM image transformation between post lithography and post etch accurately enough for practical applications.