>When Intel finally shipped it at the 45-nanometer node in 2007, Gordon Moore called it the biggest change in transistor technology since the late 1960s. The breakthrough was not the material. It was learning how to process the material at scale.
Its curious that they picked this example. The challenge with HKMG was not the material itself, but how to integrate into into the transistor stack.
There were two completely different approaches: Gate first and replacement gate. Gate first is what the industry was already using for silicon oxide so everybody tried to go with as little change as possible. Only intel decided for replacement gate, which worked much better and reaped some other benefits on the way.
This was a watershed moment in the industry and ultimately led to some of the players dropping out of the cmos race.
But is this really a "scale-up" problem? It required development of novel manufacturing processes (atomic layer deposition), but was still mainly a process integration and device engineering topic.
The part of the thesis I have to agree with is that there is a data problem. The development above relies on executing lots of time consuming and tedious split experiments that often cannot be parallelized. The outcome of this relies heavily on the experience and diligence of the experimenters.
It's probably well suited for an "autoresearch" approach, bridging to the phyiscal world and dealing with the timescale is the challenge.
hm.. has been quite a while for me. The good thing about the Tang Nano is that it is supported by the Yosys open source toolchain. There are quite a few resources on the web when you search for the combination.
I had Opus 4.5 design an LLM inference engine in verilog, including firmware and automated verification a while ago: https://github.com/cpldcpu/smollm.c
It's of course far from optical. But lowering the implementation through the abstraction levels turned out to be extremely powerful.
Yeah, that pattern can be seen everywhere in semiconductors. E.g. the transistor invention vs. Lilienfeld, Heil, Matare etc. So the scope is more narrow than "Inventend Semiconductors".
Generally, there seems to be a tendency to disregard discoveries from outside the US. I think this pattern can still be observed today...
Other examples: Invention of light bulb, telephone.
Wow! And it also implements a very interesting variant of SUBLEQ that is turing complete.
>This VM implements an OISC - a One Instruction Set Computer. That instruction takes three signed 32-bit operands, a, b and c, and runs a program from memory m[] as follows:
1 PC (program counter) starts at 0
2 Fetch the next instruction (32-bit signed operands a, b and c)
3 If the low bit on any operand is set, remove it, and replace that operand with m[operand] i.e., a dereference of that address
4 Set m[b] = m[b] - m[a]
5 If m[b] is 0 or negative, set the PC to c, otherwise increment PC by 3 words
What do you mean with "open-source"? Of course, the inference code for all the open weight models is publically available - see llama.cpp or hf transformers.
There are, however, very few models where also the full training pipeline is available. Olmo by AI2 comes to mind.