White Papers

The extension of 193nm exposure wavelength to smaller nodes continues the trend of increased data complexity and subsequently longer mask writing times. We review the data preparation steps post tapeout, how they influence shot count as the main driver for mask writing time and techniques to reduce that impact. The paper discusses the application of resolution enhancements and layout simplification techniques; the fracture step and optimization methods; mask writing and novel ideas for shot count reduction. The paper will describe and compare the following techniques: optimized fracture, pre-fracture jog alignment, generalization of shot definition (L-shot), multi-resolution writing, optimized-based fracture, and optimized OPC output. The comparison of shot count reduction techniques will consider the impact of changes to the current state of the art using the following criteria: computational effort, CD control on the mask, mask rule compliance for manufacturing and inspection, and the software and hardware changes required to achieve the mask write time reduction. The paper will introduce the concepts and present some data preparation results based on process correction and fracturing tools.

SEM contours are used to complement CD measurements in OPC model calibration. This is done to capture 2D information about printed features into the model while CD measurement data is kept to maintain accuracy for 1D features. As the method progresses, there are emerging challenges that are normally not found in CD based calibration. One such challenge is the need to align SEM contours with calibration features. This is particularly important in determining model accuracy since contour calibration typically involves a cost function that compares the SEM contours to the simulated print images.

This work explores a technique to include contour alignment errors into the calibration cost function. For each contour and its corresponding simulated print, the cost function returns an error value for a given set of model parameters. The error represents how well the model simulation compared to input contour. In addition, it also contains information on how far or how close the contour is aligned to simulation. Misalignment is to be eliminated on the fly during calibration and to be reported at the end of calibration.

In this paper we describe the proposed technique and compare the results of calibration between aligned and misaligned contour data.

The continuous reduction of device dimensions and densities of integrated circuits increases the demand for accurate process window models used in optical proximity correction. Beam focus and dose are process parameters that have significant contribution to the overall critical feature dimension error budget. The increased number of process conditions adds to the model calibration time since a new optical model needs to be generated for each focus condition. This study shows how several techniques can reduce the calibration time by appropriate selection of process conditions and features while maintaining good accuracy. Experimental data is used to calibrate models using a reduced set of data. The resulting model is compared with the model calibrated using the full set of data. The results show that using a reduced set of process conditions and using process sensitive features can yield a model as accurate as the model calibrated using the full set but in a shorter amount of time.

During optical proximity correction (OPC), layout edges or fragments are migrated to proper positions in order to minimize edge placement error (EPE). During this fragment migration, several factors other than EPE are a part of the cost function for optimal fragment displacement. Several factors are devised in favor of OPC stability, which can accommodate room for high mask error enhancement factor (MEEF), lack of process window, catastrophic pattern failure such as pinch/bridge and improper fragmentation. As technology nodes becomes finer, there are conflicts between OPC accuracy and stability, especially for metal layers. To address this, several techniques have been introduced, including target smoothing, process window-aware OPC, model-based re-targeting, and adaptive OPC. Those techniques invoke additional edges, or fragments, prior to correction or during OPC iteration. As a result, the number of jogs – and shot count – are dramatically increased. There is trade-off relationship between data complexity and various methods for OPC stability. In this paper, those relationships have been investigated. Mask shot count reduction is achieved by reducing the number of jogs with jog smoothing. The effect of jog smoothing on OPC output – in view of OPC performance and mask data preparation - was studied quantitatively for respective technology nodes.

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.

With rapidly shrinking feature sizes, full chip robust Optical Proximity Correction (OPC) will take longer due to the increasing pattern density. Furthermore, to achieve a perfect OPC control recipe becomes more difficult. The critical dimension of the design features is smaller than the exposure wavelength, and there is only limited room for the OPC correction. Usually very complicated scripts need to be developed to handle the shrinking designs. So when you are defining a parameter value in your OPC control recipe, one problem is how to find the optimum setting. Usually there are many parameters in the script, some of which may have impact on others' performance. We demonstrate an approach to optimizing the critical parameters settings with cost functions to reduce the difficulty of OPC recipe development.