Sub-Resolution Assist Feature (SRAF) printing detection is critical during SRAF model building. Currently, SRAF printing detection on silicon wafer is mainly through human judgement on CDSEM images, which is inefficient and error prone. Therefore, a robust automatic SRAF printing classification mechanism is essential to improve detection accuracy and efficiency. This paper presents a method of classifying SRAF printing based on a database-independent contour extraction algorithm. By size calculation on extracted contour SRAF feature printing classification can be made automatically. This flow has been demonstrated to be able to correctly classify SRAF printing with consistent performance thus avoid the subjectivity and inconsistency in human judgement.
CDSEM metrology is a powerful tool to obtain silicon data. However, as our technology nodes advance shrink to 14nm and below, the CD measurement data from CDSEM can hardly provide sufficient information for OPC verification (OPCV) and the related silicon verification. On the other hand, the abundant information from CDSEM images has not been fully utilized to assist our data analysis. In this context, contour extraction emerges as the best method to obtain extensive information from CDSEM images, especially for 2D structures. This paper demonstrates that contour extraction bridges the gap between the needs of 2D characterization and the limited capability of CDSEM measurement. The extracted contour enables automatic identification of litho-hotspots using OPCV tools, especially for non-CD related hotspots. Statistical silicon data extraction and analysis on complex geometries is viable with extracted contours. The silicon data can then be feedback to the evolution of non-CD OPCV checks, where simple CD measurement is inadequate. Effective CD can also be calculated from the obtained 2D information, with which Bossung curves can be built and provide complementary information.
The ever increasing pattern densities and design complexities make the tuning of optical proximity correction (OPC)
recipes more challenging. There are various recipe tuning methods to meet the challenge, such as genetic algorithm
(GA), simulated annealing, and OPC software vendor provided recipe optimizers. However, these methodologies usually
only consider edge placement errors (EPEs). Therefore, these techniques may not provide adequate freedom to solve
unique problems at special geometries, for example bridge, pinch, and process variation band related violations at
complex 2D geometries.
This paper introduces a general methodology to fix specific problems identified at the OPC verification stage and
demonstrates its successful application to two test-cases. The algorithm and method of the automatic scoring system is
introduced in order to identify and prioritize the problems that need to be fixed based on severity, with the POR recipe
score used as the baseline reference. A GA optimizer, whose objective function is based on the scoring system, is
applied to tune the OPC recipe parameters to optimum condition after generations of selections. The GA optimized
recipe would be compared to existing recipe to quantify the amount of improvement.
This technique was subsequently applied to eliminate certain chronic OPC verification problems which were
encountered in the past. Though the benefits have been demonstrated for limited test cases, employing this technique
more universally will enable users to efficiently reduce the number of OPC verification violations and provide robust