The Enchippening

Unlike the software world (which has great open-source foundations for a lot of the core tools used, such as compilers and operating systems), many of the software tools used in electronics design automation (EDA) and semiconductor fabrication are developed by industry giants at high costs (edit: high costs to the users), with little consideration given to the quality and performance of the software itself (not all parts are bad, but typically most are).

Fortunately, academia and open source have been accelerating progress within the EDA stack with long-standing tools like Yosys, full-stack academic projects for tape out with good backing like OpenROAD, and workgroups like CHIPS Alliance.

However, I believe that there is still a gap between academic + open-source efforts and industry research. To really enchippify everything, the academic and open-source communities need more open and accessible tools for people to use, without having to rely solely on industry tools, funding, and higher motives.

In other fields, a hobbyist can do wood/metalworking or learn programming or build a robot kit. There's an onramp for people to start learning the skills, which makes a huge ecosystem of gradually improving talent.

But in microfabrication, even though it's the only way to make chips, screens, cameras, inkjets, and LEDs, the minimum equipment cost is millions of dollars. Even worse, it takes even professionals months to fine-tune a manufacturing process to make a new thing.

As a result, R&D is much lower than it could be, and most fabrication is limited to circumstances with a high chance of mass production payoff.

I think many people in business sort of stick to the status quo. We just got where we got, and lets just rest a bit and turn the crank. But if the roadmap says things will be 1.9x in 17 months, the projects that get the greenlight will be different.

I don't think predictors like this are common or visible. A lot of business is reactionary, to competition achieving something.

Additionally, it also helps with tech dependencies. Like "if I plan for chips to be this fast in this many months, our project can make these assumptions"

How cheap is that? Cheap enough to do it at home?

Because that's something we really need right now. We should be able to fabricate our own integrated circuits at home just like we can make software at home. Democratization and decentralization of this technology would be world changing and would protect us against numerous government and industry interests. It would be enough to safeguard computer freedom forever.

Other technologies might have some improvements from miniaturization, but I don't think the principle applies for solar cells. You obviously can't just shrink a cell and keep the same output[1]. What you can do is learn how to shave costs and improve efficiency, and also amortize R&D. Eventually you reach somewhere with very good efficiency and costs close to the raw material cost.

[1] That said, maybe you can shrink the thickness of a cell? That being the case, at one point just the cost of the frame for keeping the cell integrity (specially under wind loads etc.) would start dominating costs, and also the (copper) wiring outputting power from cells. I wonder how far we are from this being the case?

Wind power is interesting because it's mostly a mechanical problem (or at least there's a significant mechanical component). You have to transport the forces on a moving blade, or some kind of moving part (it has to be moving due to P=F.v), so all those parts have a minimum thickness necessary to offload those forces to the ground. It would increase mechanical efficiency (by requiring less load for a given power) if you could increase the velocity of the moving parts, but wind speed itself is limited. You could try funneling the wind, but that introduces structures which themselves suffer stresses (and also induce drag lowering efficiency). There's a limit (at material/W) given by wind speed, and specific strength of the structural material.

This very long exponential improvement (miniaturization) march is a somewhat unique effect to information processing[2], because information can in principle be represented at any scale. That's not to say that all those technologies can't be significantly improved. Generally automation, mass production, along with (limited) efficiency improvements and general cleverness can go a very long way.

[2] I don't mean to be taken as law: there are probably also applications (outside information processing) that you can only do well with the benefit of certain scales. For example, the cells of our bodies are wonderful mechanisms whose magic really works because they can each play tiny mechanisms and deploy molecular-scale objects to act on such tiny scales, and the billions of them make machines that have no parallels in terms of large-scale fabrication (including self-repair, filtration of compounds, ...).

Integrated circuit manufacturing scales well because the cost per transistor is lower with every process improvement, so as process improvements accumulate, more value (or perhaps more productivity) per wafer is created. This strictly applies to digital circuitry - most analog circuitry requires a certain geometry or process technology not available on a massively optimized modern digital process. While some analog technology improvements are made, it's at a much slower pace. Moreover, a lot of analog redesigns come about as a means to abandon older, less cost-effective semiconductor technology - so the argument that cheap older tech is a boon to any industry outside of limited volume and high cost R&D is suspicious.

Solar cost improvements are in large part a function of massive economies of scale coupled with cheap manufacturing costs and government subsidies from the primary country of origin (which country? Take a guess). I'm sure there's been improvements in a handful of solar material designs, but if you make a large enough volume of panels in a country with cheap labor and massive government subsidies, costs go down. It's not rocket science. To the extent that modern semiconductor technology has improved solar, maybe an argument can be made for improved digital inverter controllers and for silicon carbide FETs maturing enough to make high-voltage panels efficient and ubiquitous at grid-scale deployments. But it's mostly volume, cheap labor costs, and government subsidies.

I don't know about biotech. The following is speculation. I think a lot of biotech was feasible 25 years ago in terms of manufacturability of microfluidics or molecular identification circuitry; but the cost of mass manufacture, and particularly the compute requirements to quickly recover signal from a highly noisy process, wasn't economical or simple to build until digital signal processing and general purpose compute became cheap and fast enough to integrate alongside the biosensors, often as copackaged modules, or generic coprocessors (ARM, RISC-V, etc) that can be built on a ton of different processes and which export raw sensor data quickly enough to allow much heavier duty professors to do the bulk of the work.

I think actual fabrication tech improves slowly, because at its core it's chemistry and material science which also improves slowly (see: batteries). Digital circuitry is a mind-blowing exception, thanks to the incredible amount of usable abstractions that can be built from the manufacture of two specific transistor recipes. But digital circuitry is so powerful, it becomes possible to abstract away massive chunks of otherwise difficult problems in software, firmware, or dedicated transistor-level algorithm implementations, which reduces the amount of physical-world improvements needed to make useful innovations.

> Wright found that every time total aircraft production doubled, the required labor time for a new aircraft fell by 20%. This has become known as "Wright's law". Studies in other industries have yielded different percentage values (ranging from only a couple of percent up to 30%), but in most cases, the value in each industry was a constant percentage and did not vary at different scales of operation. The learning curve model posits that for each doubling of the total quantity of items produced, costs decrease by a fixed proportion. Generally, the production of any good or service shows the learning curve or experience curve effect. Each time cumulative volume doubles, value-added costs (including administration, marketing, distribution, and manufacturing) fall by a constant percentage.

[1] https://en.wikipedia.org/wiki/Experience_curve_effects