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.