As the patterning of IC manufacturing shrinks to the 32-nm node and beyond, high-NA and immersion lithography are
required for pushing resolution to its physical limit. To achieve good OPC performance, various physical effects such as
polarization, mask topography, and mask pellicle have to be considered to improve the model accuracy.
The attenuation and the phase variation of TE and TM wave components induced by the pellicle would impact optical
qualities in terms of resolution, distortion, defocus shift, and high-order aberrations. In this paper, the OPC model
considering pellicle effects is investigated with Jones pupil. The CD variation induced by the pellicle effect can be
predicted accurately. Therefore, the improvement on model accuracy for 32-nm node is demonstrated.
Optical proximity correction is the technique of pre-distorting mask layouts so that the printed patterns are as close to the desired shapes as possible. For model-based optical proximity correction, a lithographic model to predict the edge position (contour) of patterns on the wafer after lithographic processing is needed. Generally, segmentation of edges is performed prior to the correction. Pattern edges are dissected into several small segments with corresponding target points. During the correction, the edges are moved back and forth from the initial drawn position, assisted by the lithographic model, to finally settle on the proper positions. When the correction converges, the intensity predicted by the model in every target points hits the model-specific threshold value. Several iterations are required to achieve the convergence and the computation time increases with the increase of the required iterations. An artificial neural network is an information-processing paradigm inspired by biological nervous systems, such as how the brain processes information. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. A neural network can be a powerful data-modeling tool that is able to capture and represent complex input/output relationships. The network can accurately predict the behavior of a system via the learning procedure. A radial basis function network, a variant of artificial neural network, is an efficient function approximator. In this paper, a radial basis function network was used to build a mapping from the segment characteristics to the edge shift from the drawn position. This network can provide a good initial guess for each segment that OPC has carried out. The good initial guess reduces the required iterations. Consequently, cycle time can be shortened effectively. The optimization of the radial basis function network for this system was practiced by genetic algorithm, which is an artificially intelligent optimization method with a high probability to obtain global optimization. From preliminary results, the required iterations were reduced from 5 to 2 for a simple dumbbell-shape layout.
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.
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.