Etch Proximity Correction through Machine-Learning-Driven Etch Bias Model

摘要

Accurate prediction of etch bias has become more important as technology node shrinks. A simulation is not feasible solution in full chip level due to excessive runtime, so etch proximity correction (EPC) often relies on empirically obtained rules or models. However, simple rules alone cannot accurately correct various pattern shapes, and a few empirical parameters in model-based EPC is still not enough to achieve satisfactory OCV. We propose a new approach of etch bias modeling through machine learning (ML) technique. A segment of interest with its surroundings are characterized by some geometric and optical parameters, which are then submitted to an artificial neural network (ANN) that outputs predicted value of etch bias. The new etch bias model and EPC are implemented in commercial OPC tool and demonstrated using 20nm technology DRAM gate layer.