Proceedings PaperPrecise measurement of process bias and its relation to MEEF
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Process Bias is traditionally defined as a manufactured offset of the mask-features that induces a photoresist image size to more closely match the nominal or desired circuit design size. The metric is calculated as the difference between the size of the image on the wafer and the mask with image reduction taken into consideration. Optical process corrections (OPC) in the mask design must consider not only the Bias but also the influence of aerial image artifacts such as near-neighbor proximity, polarization and birefringence. The interactions are further complicated by the wavefront's interaction with the imaging media and optical interactions with the translucent film stack on the wafer. With the increased frequency of resolution enhancement (RET) artifacts on the mask, the concept of Bias as a simple scalar becomes less clear. In this study Bias is shown to exhibit the anticipated systematic response to all of the static exposure conditions of the process. Variations across each field-of-exposure however behave nonlinearly with the range of fluctuations encountered within the process-space experienced during device manufacture. A model is developed that allows the Bias response to be comparatively measured for each mask feature-design that characterizes not only the behavior at optimum exposure but also each features stability across process and imaging perturbation sources. The Bias models are applied to profile metrology gathered from matrix exposure data. Fine-structure perturbations in the Bias are extracted comparing their relative variation to process fluctuations that in-turn illustrates a strong individual feature construction-sensitivity. This analysis suggests that individual feature design is a strong contributor to process-stability of a reticle. Even more significant, the Static Bias variation across the exposure field of a reticle is shown to be inversely related to the dose-uniformity map needed to achieve uniform critical features at the process-target size. A new metric is introduced to provide a means of modeling the non-linear local Bias Signature for IntraField feature perturbations as a measure of the Bias Error Enhancement Function (BEEF). The BEEF metric is shown to be relatively insensitive to variations in the manufacturing exposure process-space but strongly responsive to variations in critical feature manufacture or design. The model is then extrapolated to define the relationship between Bias Response and the Mask Error Enhancement Function (MEEF). The base design of a photomask feature is shown to be a strong contributor not only to resolution and depth-of-focus but also to the robustness of image response or it's ability to maintain stable resolution and depth of focus across the process-space. The Proper selection of different feature design alternatives can greatly reduce photomask sensitivity to process variations. The selection process for these designs as well as new reticle validation is simplified using the BEEF metric as an evaluator. "BEEF" is a metric more closely tied to process response of a reticle design than MEEF and is more easily extracted from in-process raw metrology.