Mask process matching using a model based data preparation solution

Abstract

Process matching is the ability to precisely reproduce the signature of a given fabrication process while using a different one. A process signature is typically described as systematic CD variation driven by feature geometry as a function of feature size, local density or distance to neighboring structures. The interest of performing process matching is usually to address differences in the mask fabrication process without altering the signature of the mask, which is already validated by OPC models and already used in production. The need for such process matching typically arises from the expansion of the production capacity within the same or different mask fabrication facilities, from the introduction of new, perhaps more advanced, equipment to deliver same process of record masks and/or from the re-alignment of processes which have altered over time. For state-of-the-art logic and memory mask processes, such matching requirements can be well below 2nm and are expected to reduce below 1nm in near future. In this paper, a data preparation solution for process matching is presented and discussed. Instead of adapting the physical process itself, a calibrated model is used to modify the data to be exposed by the source process in order to induce the results to match the one obtained while running the target process. This strategy consists in using the differences among measurements from the source and target processes, in the calibration of a single differential model. In this approach, no information other than the metrology results is required from either process. Experimental results were obtained by matching two different processes at Photronics. The standard deviation between both processes was of 2.4nm. After applying the process matching technique, the average absolute difference between the processes was reduced to 1.0nm with a standard deviation of 1.3nm. The methods used to achieve the result will be described along with implementation considerations, to help assess viability for model driven data solutions to play a role in future, critical mask matching efforts.