SPIE - Education Stochastic Lithography SC1263

Moore’s Law has been changing the world for over 50 years, and advances in lithography have been a (the) major factor in its success. The success of lithography scaling, however, may cause the undoing of Moore’s Law as smaller features become susceptible to stochastics variations such as linewidth roughness, local critical dimension uniformity, and stochastic defects. This course will look at how stochastic variation during lithography affects semiconductor devices, how to measure stochastic variations, the major causes of stochastic variation, and what stochastics will mean for the future of lithography scaling. 1. Introduction to Line-Edge Roughness (LER) and Linewidth Roughness (LWR): LER Experimental Results, Device Effects, LER Trends 2. Metrology for LER/LWR: Power Spectral Density Measurement, Low-frequency roughness and feature-to-feature variation, High-frequency roughness and within variation, Measuring roughness using SEM images, Simulating rough features 3. Stochastic Modeling Fundamentals – No Longer a Continuum: Discrete Random Variables, Binary Distribution, Poisson Distribution, Example – Chemical Concentration 4. A Stochastic Model of Lithography: Optical Imaging – Photon Shot Noise, Photon Absorption and Exposure, EUV Resist Exposure, Diffusion – A Random Walk, Reaction-Diffusion, Acid-Base Quenching, Development, The LER Model, Efficacy of LER post-process smoothing 5. Future Work