Sensitivity of Spectroscopic Scatterometry: Sub-100nm Technology SFR Workshop November 8, 2000 Ralph Foong, Costas Spanos Berkeley, CA 2001 GOAL: To fully characterize the capabilities of scatterometry in fulfilling the metrology needs of the 100nm technology node. 11/8/2000

Motivation Capabilities of scatterometry and required equipment specifications need to be formalized for 100nm metrology. Commercial ellipsometers have been identified as being able to perform spectroscopic scatterometry. Hence, the focus of this study is on these equipment. Precision of current generation commercial ellipsometers in measuring profiles consistent with 100nm technology node has to be confirmed. Scalability of scatterometry towards 70nm and 50nm metrology has to be explored. Minimum commercial ellipsometer specifications necessary to successfully implement 70nm and 50nm metrology need to be determined. 11/8/2000

Which part of the spectrum contains the most information? Overall Framework of Sensitivity Analysis Commercial Equipment Analysis Profile Parameters Simulations for variation in parameter X [X(-),X(Nominal), X(+)] Cos D Lambda Tan Y Determine Noise Contributions Tan Y, Cos D Noise Spectrum Are Variations Detectable? NoYes EM Response Variations Which part of the spectrum contains the most information? 11/8/2000

Methodology Electromagnetic simulations are conducted for small changes in profile parameters to measure variations in EM response. Noise analysis of commercial ellipsometers is carried out to determine detectability of EM response variations. Rounding d(IDetector) d(ISource) Slope Angle Height PR CD d(qAnalyzer) Footing d(qPolarizer) ARC d(Beam Divergence) Poly-Si Sample Si 11/8/2000

Signal-to-Noise Ratio for SOPRA Ellipsometer Intensity fluctuation is the main contributor of measurement noise in ellipsometers. Monte-Carlo simulations incorporating intensity fluctuations are used to determine the final distributions of Tan Y and Cos D. The ‘Minimum Detectable Variation’ lines represent the sum of the 3s errors of each of the 2 profiles measured to obtain the variation. The graphs demonstrate a trend toward significant information contained in a narrow band in the lower wavelength spectrum. Signal averaged over 30 measurements Noise represents 1s standard deviation for each wavelength Empirical formula for signal-to-noise ratio: Noise = 0.412(Intensity)0.632 (R2 Value = 0.937) 11/8/2000

100nm Technology Simulations 100nm Dense Lines (ASIC) 65nm Isolated Lines (MPU) Detectable (Above yellow line) Undetectable (Below yellow line) Detectable (Above yellow line) Undetectable (Below yellow line) spectrum of information content 11/8/2000

Detectable (Above yellow line) Undetectable (Below yellow line) 70nm Technology Simulations 70nm Dense Lines (ASIC) 45nm Isolated Lines (MPU) Detectable (Above yellow line) Undetectable (Below yellow line) 11/8/2000

Detectable (Above yellow line) Undetectable (Below yellow line) 50nm Technology Simulations 50nm Dense Lines (ASIC) 30nm Isolated Lines (MPU) Detectable (Above yellow line) Undetectable (Below yellow line) 11/8/2000

2002 & 2003 Goals Study the feasibility of building 100nm capable profile extraction using small footprint, in-line spectroscopic ellipsometry, by 9/31/2002 Implement lithography controller that merges full profile in-line information with available metrology, by 9/31/2003 Profile Diagnostics DUV Photolithography PR Deposition, Focus, Exposure, Bake Time, Development Time, etc Process Flow In-Line Scatterometry Wafers Feedback Control Loop 11/8/2000