Prediction of biases for optical proximity correction through partial coherent identification

Most approaches to model-based optical proximity correction (OPC) use an iterative algorithm to determine the optimum mask. Each iteration requires at least one simulation, which is the most time-consuming part of model-based OPC. As the layout becomes more complicated and the process conditions are driven to the physical limit, the required number of iterations increases dramatically. To overcome this problem, we propose a method to predict the OPC bias of layout segments with a single-hidden-layer neural network. The segments are characterized by length and based on intensities at the corresponding control points, and these features are used as input to the network, which is trained with an extreme learning machine. We obtain a best-error root mean square of 1.29 nm from training and test experiments for layout clips sampled from a random contact layer of a logic device. In addition, we reduced the iterations by 27.0% by initializing the biases in the trained network before performing the main iterations of the OPC algorithm.