Nvidia and Computational Inverse Lithography Technology

Photolithography, the fundamental technology used in almost all integrated circuit manufacturing, is pretty simple in concept: you make a mask for the pattern you wish to lay down on the chip, apply a photo-sensitive resist uniformly to the chip, expose it to light through the mask, wash away the portion the mask indicates should be processed in the next step, and then etch, deposit material, or implant material on the non-masked regions. The details of this are exquisitely complicated and require great precision, but the idea of manufacturing complicated devices by what amounts to printing pictures of them is the simple concept that underlies the greatest revolution in mass production in human history.

Since their inception in the late 1950s, integrated circuits have simultaneously shrunk the size of the devices that compose them while increasing their density and complexity. I remember, in the late 1960s, reading forecasts that this process would have to come to an end when the device geometries shrank to smaller than the wavelength of light used to print the mask onto the chip substrate. Well, that didn’t take into account the cleverness of engineers, physicists, optical designers, and creators of computer-aided design tools engaged in a global competition to produce the fastest, least expensive, and most complex and energy efficient circuits in a market which was growing exponentially in size.

If you think in terms of geometrical optics, it’s completely impossible to create a sharp image with features smaller than the wavelength of light you’re using to produce it. This is why, for example, there is an absolute limit on the smallest object you can observe through a visible light microscope—to see smaller things, you need to use something like an electron microscope, which images with a far smaller wavelength. But geometrical optics considers light as composed to infinitely small rays that may be absorbed, reflected, and bent, but do not exhibit the properties due to their wave nature such as diffraction and interference. As light interacts with small features on the order of its wavelength, these phenomena increasingly dominate, and shining light through a mask with features comparable to its wavelength will produce an image that looks nothing like the pattern on the mask.

But, since light propagation is time symmetric, if you ran the light the other way, wouldn’t you get back the pattern on the mask? Well, in principle, but of course there are nasty details to be dealt with. If you could solve them, though, couldn’t you then compute the pattern on a mask which, after all the messy diffraction and interference occurred, actually printed the pattern you wanted on the chip? The mask would look nothing like the pattern on the chip, but it would make that pattern when light shone through it.

This was one of those things that were possible in principle, but required such a hideously large amount of computation that it was long considered infeasible. Over the years, chip designers learned tricks to eke more resolution out of light waves, but eventually this hocus-pocus was approaching the limit. Here is the evolution of masks to make simple horizontal and vertical traces over the years.


That screwball pattern at the right is the result of “Inverse Lithographic Technology” (ILT) or, as you might say, “Beating light into submission with a computer”. When you shine light through that crazy ILT mask, what you get on the chip are the lines at the very left.

The computation required to do this is daunting, especially when you consider that leading-edge chips can have billions of transistors, passive components, and interconnects, all of which must be perfect or the chip is junk. In March 2023, Nvidia, known for its graphics processing units (GPUs) and artificial intelligence accelerators, announced a new set of algorithms, collectively called cuLitho, which they claim speed up the computations of inverse lithography by a factor of forty, making it practical to deploy widely in chip design and mask making. Here is an IEEE Spectrum article about the announcement.

Nvidia claims that using cuLitho, a cluster of 500 Nvidia DGX H100 GPUs can compute masks using inverse lithography faster than an array of 40,000 of the fastest general-purpose processors available today, producing three to five times as many masks a day.

Taiwan Semiconductor Manufacturing Company (TSMC) is said to be qualifying the cuLitho process for production use this year.


Nvidia is working on a new lineup of artificial-intelligence chips customized for China as the semiconductor giant tries to maintain access to a huge market while adjusting to shifting U.S. regulations.

“It’s going to cost a lot of money and burn a lot of power relative to using H100s, but if I’m China, maybe I’ve got no choice,” Rasgon said.


NVIDIA basically compressed 30 years of its corporate memory into 13B parameters. Our greatest creations add up to 24B tokens, including chip designs, internal codebases, and engineering logs like bug reports. Let that sink in.

The model “ChipNeMo” is deployed internally, like a shared genie:

  • EDA scripts generation. EDA stands for “Electronic Design Automation”, a core software suite for designing the next-gen GPUs. These scripts are the keys to a $1T market cap
  • Engineering assistant chatbot for GPU ASIC and Architecture engineers that understands internal hardware design specs and is capable of explaining complex design topics;
  • Bug summarization and analysis as part of an internal bug and issue tracking system;
  • Domain-finetuned retriever that achieves much better accuracy over internal knowledge.

And we publish a whitepaper to share ChipNeMo’s creation process: https://arxiv.org/abs/2311.00176


Skip to the emergence of sovereign GPU clusters and it starts to read a bit like Snow Crash.


This video has some interesting background on the enormous complexity of the latest photolithography machines. https://youtu.be/rdlZ8KYVtPU?si=Xt0L73QGlz9WO4xG

The video before this in the channel goes into more detail about the dominance of diffraction in mask imaging.