Day two began at 8:00am with four papers I wanted to see, a philosophical problem known as multilocation.  With no best way to decide, I threw a d4 die and landed at the talk by Kenji Yamazoe of TSMC.  It turned out to be a fun choice since I loved the rigorous mathematical derivation he gave to define the theoretical maximum NILS (normalized image log-slope) versus corner rounding radius for the aerial image of a corner.

David Fried of Lam Research discussed his company’s massive efforts to create “virtual twins” of Lam equipment.  What is a virtual twin?  As used by Fried, it is what we used to call multiscale modeling.  Thus, a virtual twin of an etch tool would model that tool at the equipment scale (mechanical drawings, power consumption, throughout), reactor scale (chamber physics of flows and energy leading to wafer uniformity of reactants), near-feature scale (etch behavior as a function of feature density), the feature scale (simulation of the 3D etched patterns), and the subatomic scale (molecular modeling of the chemistry).  An effective virtual twin leads to “virtual experimentation” – running the model.  At different scales this could lead to better chamber design or an optimized etch recipe.  A quote from the presentation: “Edge placement error is really what limits scaling.”

Bob Socha gave my favorite talk of the conference so far: “Simulation-driven lithography innovation: honoring the legacy of Prof. Andrew R. Neureuther.” Prof. Neureuther died last summer after a brief illness at the age of 84, leaving behind massive accomplishments in lithography and patterning and generations of students indebted to him.  Bob did a fantastic job of capturing this legacy both from a technical and a personal level.  I too am indebted to Andy for his inspiring work and his friendship over many years.  He is missed.

Gopal Kenath of IBM discussed linewidth roughness (LWR) versus focus as the limiter of focus tolerance in gate single patterning using 0.33 NA EUV.  While the industry has come to rely on two-beam imaging (through off-axis illumination) to maximize depth of focus, Gopal revisited the trade-offs of two-beam versus three-beam image in light of stochastics.  With three beams (think conventional illumination) we have higher NILS near best focus, but a faster fall-off with focus compared to two-beam imaging.  But if LWR limits focus tolerance, does anything change in this trade-off?  Probably not, but it is worth considering using a stochastics focus.

Many people have been talking about ASML’s announcement of a 1000-Watt EUV light source, and Haining Wang gave a talk with the details of this milestone.  Specifically, ASML has shown stable operation of the source for one hour under full dose control.  He noted that this milestone for their 600W source was announced in 2023, and that source began shipping to customers two years later.  How was 1000W achieved?  Lots of optimizations and improvements were required, but the main factor was the repetition rate of the laser and tin droplet generator, which increased from 62 kHz to 100 kHz.  The rate at which these droplets are produced, then blasted to oblivion to produce light, is astounding.  The management of the heat when this intense light is reflected off the many mirrors in the system is no small feat either.

Bernardo Oyarzun of ASML discussed a recurring theme, that focus tolerance is limited by stochastics.  Using e-beam defect inspection over a large enough area to achieve one part per million defect capture rates, he showed how the “defect-free depth of focus” can be used to characterize a patterning process.

By the afternoon, I was listening to many machine learning (ML) papers (not my favorite way to spend an afternoon, but unavoidable at this conference given the very large number of papers on the topic).  Talks on image denoising in particular do not excite me, but there are some very good applications of ML worth discussing.  As I mentioned in my post yesterday, ML is especially good at interpolation, but a second major application is as a correlation engine.  Fabs have for decades looked for correlations between metrology data and sensor signals to device yield and performance.  ML can do such correlation searches even better, including massive context data as described by Sven Boese of KLA.

Saumaya Gulati of Lam gave one of the many, many Lam Research talks this week on “3D engineered” dry resists.  Dry deposition of a resist provides a unique opportunity to tailor resist properties (in particular absorption) as a function of depth, and that can be used to affect many outcomes.  I liked Gulati’s addition of line wiggling to the list of outcomes worth considering and optimizing.

But CAR (chemically amplified resist) is not without its depth-dependent knobs.  B. Rafael-Naab of Qnity (a spinout of DuPont’s electronics materials business with a name I’m not sure I will ever get used to) showed that absorption in a CAR can be increased with the addition of fluorine.  The resulting absorbed energy gradient can lead to top loss and heavily sloped profiles at the typical 50 nm resist thickness.  However, by tweaking PDQ (photodecomposable quencher) formulation/polarity to affect its attraction to the top of the resist film while minimizing other compositional gradients, a vertical profile can be achieved even for this higher absorption.

Toshiya Okamura of EMD gave a third alternative (neither CAR nor metal-oxide resist) for pushing the resolution limit of EUV.  Their MRX is a small molecule, non-CAR, crosslinking negative tone material with the additional benefit of being PFAS free.  The material seemed to be based on free radical chain reactions to achieve the needed sensitivity.  With a 20 nm resist thickness, the 24 nm pitch line/space patterns from 0.33 NA EUV printing looked reasonably good.

I dedicated my afternoon to the resist conference, though it meant I missed the talks and discussion in the “future of EUV” session going on at the same time.  It was worth it, however, if nothing else but for the great talk by Chenyun Yuan of Cornell.  One way to address the resist’s role in stochastics is to reduce compositional variation.  Yuan did that is two ways, by making a monomolecular resist (a single component), and by making that polymer “sequence-defined”, meaning that every individual component is attached to the backbone of the polymer at the same spot for each polymer.  The polypeptoid resist that he made has no additional sensitizer, is negative tone, does not require post-exposure bake, and is spin coated to about 25 nm thickness.  Initial printing results look very encouraging, and I am looking forward to seeing further progress of this material.

Since I spent the afternoon listening to resist talks, I felt I had earned the hospitality of the resist companies as I went to their parties that night.  As the dolphins once said, “Thanks for all the fish.”