Optical Proximity Correction Tool Research Articles

Optical proximity correction by using unsupervised learning and the patch loss function.

The miniaturization of nodes poses new challenges in semiconductor manufacturing. Optical proximity correction (OPC) is typically performed to satisfy technical requirements through iterative optimization. However, this method is expensive and slow. This study proposes a framework based on patch loss and a generative adversarial network through unsupervised learning to address these problems. The target pattern is used as the input of the model to avoid dependence on OPC tools. Thus, a fast approach is proposed for realizing OPC swiftly.

Laser Process Proximity Correction for Improvement of Critical Dimension Linearity on a Photomask

We report on the improvement of critical dimension (CD) linearity on a photomask by applying the concept of process proximity correction to a laser lithographic process used for the fabrication of photomasks. Rule-based laser process proximity correction (LPC) was performed using an automated optical proximity correction tool and we obtained dramatic improvement of CD linearity on a photomask. A study on model-based LPC was executed using a two-Gaussian kernel function and we extracted model parameters for the laser lithographic process by fitting the model-predicted CD linearity data with measured ones. Model-predicted bias values of isolated space (I/S), arrayed contact (A/C) and isolated contact (I/C) were in good agreement with those obtained by the nonlinear curve-fitting method used for the rule-based LPC.