Process models have been in use for performing proximity corrections to designs for placement on lithography masks for a number of years. In order for these models to be used they must provide an adequate representation of the process while also allowing the corrections themselves to be performed in a reasonable computational time. In what is becoming standard Optical Proximity Correction (OPC), the models used have a largely physical optical model combined with a largely empirical resist model. Normally, wafer data is collected and fit to a model form that is found to be suitable through experience. Certain process variables are considered carefully in the calibration process-such as exposure dose and defocus -while other variables -such film thickness and optical parameter variations are often not considered. As the semiconductor industry continues to march toward smaller and smaller dimensions -with smaller tolerance to errorwe must consider the importance of those process variations. In the present work we describe the results of experiments performed in simulations to examine the importance of many of those process variables which are often regarded as fixed. We show examples of the relative importance of the different variables.
One of the major problems in the RET flow is OPC recipe creation. The existence of numerous parameters to tune and the interdependence between them complicates the process of recipe optimization and makes it very tedious. There is usually no standard methodology to choose the initial values for the recipe settings or to determine stable regions of operation. In fact, parameters are usually optimized independently or chosen to resolve a certain issue for a specific design without quantifying its effect on the quality of the recipe or how it might affect other designs. Another problem arises when a quick fix is needed for an old recipe to build new design masks, and this causes the stacking of many customization statements in the OPC recipe, which in turns increases its complexity. Consequently, the experience of the developer is highly required to build a good as well as a stable recipe. In this context, simulated annealing is proposed to optimize OPC recipes. It will be shown how many parameters can be optimized simultaneously and how we can get insight about the stability of the recipe.
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