Stochastic Lithography

Level: Intermediate Length: 7 hours Format: In-Person Lecture Intended Audience: Scientists, engineers, technicians, or managers who wish to learn more about the impact of stochastics on lithography for semiconductor manufacture. Undergraduate training in engineering or science is assumed, as is some existing familiarity with the basic practices of lithography. Description: 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 Learning Outcomes: This course will enable you to: - describe impact of pattern roughness on device performance - explain the use of the power spectral density for the frequency characterization of roughness - explain the role of noise in biasing the measurement of roughness, and the various techniques for removing this bias - identify when the continuum model for lithography is appropriate, and when a stochastic model is required - use the mathematics of the Poisson distribution to describe basic stochastic phenomenon such as photon and chemical concentration shot noise - list the major sources of stochastic variation and what can be done to limit their effects Instructor(s): Chris A. Mack developed the lithography simulation software PROLITH, and founded and ran the company FINLE Technologies for ten years. He then served as Vice President of Lithography Technology for KLA-Tencor for five years, until 2005. In 2003 he received the SEMI Award for North America for his efforts in lithography simulation and education and in 2009 he received the SPIE Frits Zernike Award for Microlithography. He is a fellow of SPIE and IEEE and is also an adjunct faculty member at the University of Texas at Austin. In 2012 he became Editor-In-Chief of the Journal of Micro/Nanolithography, MEMS, and MOEMS (JM3). In 2017 he cofounded Fractilia, where he now works as Chief Technical Officer developing metrology solutions for the measurement of roughness. John S. Petersen is a current SPIE Fellow and a past SEMATECH Fellow with 35-years of experience in advanced lithography where he’s published more than sixty-eight papers, given numerous invited talks, taught many professional classes and holds eight patents in optical lithography and microscopy. John joined Texas Instruments in 1980, Shipley Company in 1983 and SEMATECH in 1996. In late 1998 he formed Petersen Advanced Lithography and is a co-founder of Periodic Structures where he is researching and developing optically based multi-color super resolution lithography for the 10nm pitch and high speed super resolution microscopy. In 2017 he joined imec as a senior researcher. Event: SPIE Advanced Lithography + Patterning 2023 Course Held: 26 February 2023