This thesis provides an opportunity to develop statistical methods to optimize and calibrate lithography models used to generate optimal photomask designs by mean of optical proximity correction (OPC).
Microelectronic devices with high circuit density are in high demand and are extensively researched and pursued by industries. One way to achieve higher circuit density is to decrease pattern dimension or pitch. However as pattern dimension decreases, fabrication challenge increases. Resolution Enhancement Technique (RET) such as OPC has therefore to be used to generate photomask of such circuits.
OPC aims to improve the wafer pattern fidelity by compensating errors arising due to optical or process effects during fabrication steps. To implement this correction, a lithography model has to be generated taking into account the exposure system and photo resist characteristics. These models are calibrated using very large volume of experimental data which includes CD-SEM measurements and contour extracted from SEM images. The data acquisition and image post processing is a bottleneck in model calibration flow, consuming huge amount of time and resources.
During the period of thesis, work will be focused on:
Innovative test patterns to optimize input data for model calibration
Statistical and algorithmic optimization of model calibration flow
Impact of experimental data variability on lithography models