Matrix-OPC with fast MEEF prediction using artificial neural network

Abstract

Matrix-OPC is used to mathematically derive mask bias using mask error enhancement factor (MEEF) matrix. Since MEEF denotes the edge placement error (EPE) change of segment induced by unit mask bias of its neighbor segment, exact MEEF calculation requires lithography simulation before and after perturbing neighbor segments. Therefore, MEEF calculation is a computationally expensive process, which leads to matrix-OPC being applied to only some critical regions in a layout. We propose fast MEEF prediction using an artificial neural network (ANN). MEEF represents the effect of one segment on another, so polar Fourier transform signals extracted from both segments are used as input of ANN. Also, the distance between two segments and the direction of each segment is used as input of ANN. Predicted MEEFs are used to construct MEEF matrices and matrix-OPC is used to derive mask biases. Experimental results show that proposed MEEF prediction is 3.7 times faster than exact MEEF calculation, thus matrix-OPC with predicted MEEFs is 30% faster than matrix-OPC with exact MEEFs.