Cherng-Shyan Tsay

OPC modeling by genetic algorithm

Optical proximity correction (OPC) is usually used to pre-distort mask layouts to make the printed patterns as close to the desired shapes as possible. For model-based OPC, a lithographic model to predict critical dimensions after lithographic processing is needed. The model is usually obtained via a regression of parameters based on experimental data containing optical proximity effects. When the parameters involve a mix of the continuous (optical and resist models) and the discrete (kernel numbers) sets, the traditional numerical optimization method may have difficulty handling model fitting. In this study, an artificial-intelligent optimization method was used to regress the parameters of the lithographic models for OPC. The implemented phenomenological models were constant-threshold models that combine diffused aerial image models with loading effects. Optical kernels decomposed from Hopkin’s equation were used to calculate aerial images on the wafer. Similarly, the numbers of optical kernels were treated as regression parameters. This way, good regression results were obtained with different sets of optical proximity effect data.

Phenomena and OPC solution of ripple patterns for 65-nm node

The ripple patterns induced by the lithography process will lead to unpredictable necking or bridging risks on circuit patterns. This phenomenon is particularly severe while using the attenuated-phase-shifting mask combined with the strong off-axis illumination. The CD variation induced by the ripple effect is difficult to be accurately corrected by conventional OPC approaches. In this paper, ripples on patterning for the 65nm node have been studied and their problems solved. One of the dominant root causes of ripples is the optical side-lobes from the surrounding patterns. On the L-shape patterns for example, the ripples that occur on the horizontal lines are induced by the side-lobes of the vertical lines. Based on this study of the ripple effect, the layout types resulting in ripple patterns can be classified and predicted. An advanced OPC approach by the segmentation analysis on polygons as well as the correction algorithm optimization has been developed and applied to solve this ripple problem.

Global CD uniformity improvement in mask manufacturing for advanced lithography

The control of global critical dimension uniformity (GCDU) across the entire mask becomes an important factor for the high-end masks quality. Three major proceses induce GCDU error before after-developing inspection (ADI) including the E-Beam writing, baking, and developing processes. Due to the charging effect, the fogging effect, the vacuum effect and other not-well-known effects, the E-Beam writing process suffers from some consistent GCDU errors. Specifically, the chemical amplified resist (CAR) induces the GCDU error from improper baking. This phenomenon becomes worse with negative CARs. The developing process is also a source of the GCDU error usually appears radially. This paper reports the results of the study of the impact of the global CD uniformity on mask to wafer images. It also proposes solutions to achieve better masks.