Photomask pattern evaluation by massive measurement using Die-to-Database

Photomask pattern has been traditionally evaluated with limited gauges with 1D cut-line based measurement method on SEM (Scanning Electron Microscopy) image. After the advent of EUV lithography and multi-beam mask writer for curvilinear design, the demand of controlling pattern dimension with higher precision grows in the photomask fabrication. In order to achieve better dimension control, advanced MPC (Mask Process Correction) is applied to the photomask fabrication, and the calibration of the mask process model for the correction is essential technique. For this model calibration, it is necessary to evaluate the pattern dimension not only in limited points but also in a variety of photomask patterns including 1D pattern and 2D pattern such as corner, tip and small jogs etc. After applying MPC, monitoring critical pattern with massive sampling points is also expected to maintain stable photomask fabrication. D2DB (Die to Database) is effective method to evaluate various patterns by comparing SEM images and its layout data which can be used to define POIs (Point of Interests) and to select measurement method automatically, and it realizes massive measurement. D2DB has been widely used to collect massive pattern data printed on wafer. These data are useful to correct OPC (Optical Proximity Correction), optimize wafer process condition, and monitor hotspot, and has made a significant contribution for faster ramp up and yield enhancement. Although the application of off-line D2DB to photomask pattern has been reported, D2DB has a high computational cost, so further performance improvement in terms of throughput is necessary for the evaluation of photomask pattern with higher precision which will be required in near future. In this study, D2DB system for massive measurement of photomask pattern has been developed using the technology cultivated for evaluation of printed pattern on wafer. Images are acquired by CD-SEM and aligned to the layout data. POIs and measurement method are automatically determined and selected by analyzing layout data, and the measurement is distributed among multiple processing servers and performed inline. The obtained massive data is effectively classified by utilizing layout data and quantitative analysis of statistical photomask pattern fidelity is realized. This system enables much faster TAT from measurement to analysis compared to conventional method. Furthermore, the expected effect of consistent analysis through semiconductor manufacturing which is achieved by applying the same D2DB to photomask pattern and printed pattern on wafer is discussed.