2020-4-22

Realizing more accurate OPC models by utilizing SEM contours

• Event: SPIE Advanced Lithography, 2020, San Jose, California, United States
• Realizing more accurate OPC models by utilizing SEM contours
• Proc. SPIE 11325, Metrology, Inspection, and Process Control for Microlithography XXXIV, 1132524 (20 March 2020); doi: 10.1117/12.2554527, Chih-I Wei, Rajiv Sejpal, Yunfei Deng, Ir Kusnadi, Germain Fenger, Masahiro Oya, Yosuke Okamoto, Kotaro Maruyama, Yuichiro Yamazaki, Sayantan Das, Sandip Halder, Werner Gillijns, "Realizing more accurate OPC models by utilizing SEM contours,
  Mentor Graphics Corporation, Leuven, Belgium
  Mentor Graphics Corporation, Wilsonville, OR, U.S.A.
  TASMIT, Inc., Yokohama-shi, Kanagawa, Japan
  IMEC, Leuven, Belgium

ABSTRACT

The method to perform Optical Proximity Correction (OPC) model calibration with contour-based input data from both small field of view (SFoV) and large field of view (LFoV) e-beam inspection is presented. For advanced OPC models - such as Neural Network Assisted Models (NNAM) [1], pattern sampling is a critical topic, where pattern feature vectors utilized in model training, such as image parameter space (IPS) is critical to ensure accurate model prediction [2-5]. In order to improve the design space coverage, thousands of gauges with unique feature vector combinations might be brought into OPC model calibration to improve pattern coverage. The time and cost in conventional Critical Dimension Scanning Electron Microscope (CD-SEM) metrology to measure this large amount of CD gauges is costly. Hence, an OPC modeling solution with contourbased input has been introduced [6]. Built on this methodology, a single inspection image and SEM contour can include a large amount of information along polygon edges in complex logic circuit layouts. Namely, a better feature vector coverage could be expected [7]. Furthermore, much less metrology time is needed to collect the OPC modeling data comparing to conventional CD measurements. It is also shown that by utilizing large field 2D contours, which are difficult to characterize by CD measurements, in model calibration the model prediction of 2D features is improved. Finally, the model error rms of conventional SFoV modeling and LFoV contour modeling between SEM contours and simulation results are compared.

Key words: EUV Lithography, SEM Contour, Contour-based OPC Modeling, 5nm, Machine Learning