SPIE Advanced Lithography Symposium 2020 – day 2

Tuesday was a heavy day of stochastics for me.  Greg Wallraff of IBM got me off to a good start with his interesting simplified Monte Carlo-like stochastic resist model.  As expected for chemically amplified resists, higher PAG loading had a big effect on reducing stochastic variability, and higher amounts of photodecomposable quencher had a smaller but noticeable impact.  Also as I expected, acid amplifiers only make things worse stochastically.  All of his simulations used a 15nmx15nmx15nm voxel, but I hope he will look into the impact of voxel size on his simulation results.  I think that understanding the role of the averaging volume (voxel size essentially) is one of the biggest gaps in our knowledge of stochastic behavior.

Andy Neureuther gave a fantastic talk on the role of dissolution path in determining missing contact defectivity.  His algebraic model looked very insightful, and dissolution path plays an underappreciated role in how photon shot noise manifests itself in stochastic defectivity of contacts.  Dario Goldfarb of IBM and Patrick Theofanis of Intel each showed wonderfully rigorous experimental and simulation studies (respectively) of EUV resist exposure mechanisms.

Peter de Bisschop of imec once again provided the incentive (and the data) for the industry to look more closely at EUV defectivity versus dose, this time by adding pitch variation and challenging us to model the results.  Both Synopsis and Mentor used that same dataset to develop models for stochastic defectivity (a work still in progress).

I gave my paper for the week (comparing the noise sensitivity of different CD-SEM edge detection algorithms), as did two of my coauthors on separate studies.  Jen Church of IBM compared LER with defectivity for lines and spaces and LCDU with defectivity for contacts.  While she showed that unbiased LER and low-noise LCDU were required, these metrics alone were not enough to predict defectivity or yield.  Charlotte Cutler of DuPont gave the third in a series of papers she has presented at the Patterning Materials conference on using power spectral density (PSD) analysis for resist design.  In my completely biased perspective, both of these papers were highlights of the day.

At the metrology conference I enjoyed a talk by the National Metrology Institute of Japan on using AFM as a roughness reference metrology, even though I disagree with some of their conclusions.  Comparing SEM and AFM measurement of the same sample (an etched silicon line), the two measured edges matched extremely well except at the high frequencies.  The authors attributed these differences to SEM noise, but failed to recognize the role of instrument resolution.  With an uncharacterized tip size of about 7nm, their AFM is a much lower resolution instruments (in terms of high-frequency roughness measurement) and so was unable to see the high frequency variations that are visible in a SEM (admittedly contaminated by SEM noise).  I hope the authors will continue their work be comparing AFM to unbiased SEM measurements, and that they will work to deconvolve the tip shape from the AFM measurements (hopefully using different tips with different shapes).

The final talk I heard was a fantastic one, by Luc Van Kessel, a student at the Technical University of Delft.  He studied a subject I have long been fascinated with:  how does the 2D surface roughness of the sidewall of a feature translate into the 1D edge roughness observed in a top-down CD-SEM?  For his 300V SEM simulations, the observed top-down edge an isolated line was essentially the extreme X-Y points of the 3D feature.  Things were a bit more complicated for a small space because of the aspect ratio making the bottom of the space less visible in the SEM.  Also, his 500V simulations were only preliminary and could be somewhat different due to the greater penetration distance of those higher-energy electrons.  Great work, Luc!

With Harry Levinson, I ended the day by hosting an all-conference panel called “A toast to lithography’s past:  what we learned from technologies not used in HVM”.  Hans Loschner gave us the history of the life (and death) of ion-beam projection lithography, Reiner Garreis of Zeiss discussed 157-nm lithography, Alexander Liddle recalled his time working on Scalpel, and I filled in for Tobey Aubrey (who couldn’t make it) to talk about our lessons learned from proximity x-ray lithography.  While I enjoyed all of the discussion, I didn’t enjoy the unfortunate logistics.  We made the big mistake of scheduling our panel immediately after the EUV retrospective panel.  Not only was the EUV panel late to finish (as expected for EUV), but the time to transition between panels was far too short.  The topics of the two panels were very similar, but nobody would want to sit through four hours of panel discussions at one time.  Lessons learned not only about lithography, but about panel discussions as well.

2 thoughts on “SPIE Advanced Lithography Symposium 2020 – day 2”

  1. Here is a comment relayed to me by Hans Loeschner:

    “The final part of my presentation was “Lessions Learned” resulting with Elmar Platzgummer in electron Multi-Beam Mask Writer MBMW-101 HVM for the 7nm node in 2017, and MBMW-201 for the 5nm node in 2019. Without Elmar Platzgummer and his team this would not have happened!” – Hans Loeschner

  2. Sounds like a good meeting. And thanks for the writeups–for those of us who can’t always go, it is a pleasure to see.

    On SEM: I tend to think that a good model for secondary electrons is (per point) (material constant) * C*(derivative of height).

    It becomes much more clear in a cross sectional SEM: you see the material contrast in SE quite clearly, though it is thickness dependent to a small degree. Any surface height creates confounding signals.

    The same is true on surfaces.

    There is some multiplier, and it depends on the details of the detector and the settings. Quite to my surprise, images on a Hitachi CDSEM and a FEI SEM were wildly different, even for SE images, even after attempts to match them for equivalent QE. A good part of this is probably the source brilliance.

    I will look up the results, as this has been a longstanding curiousity.

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