Industry Deep Dive: Semiconductor Wafer Fab Equipment

In this article, we share an industry primer on the machines that enable the creation of semiconductor wafers (the silicon on which GPU/CPU chips are ‘printed’). The companies that sell these machines are few (~5 major ones) in number and underpin the entire AI industry. This enables them to generate superior returns on capital. Roughly $150 billion will be spent on wafer fabrication equipment in calendar 2026, according to our estimates, and three-quarters of it will pass through these companies.

We cover how a chip is made, where each dollar of equipment spending goes, and who collects it. We then value the four listed names we follow. We set out the levels at which we would be interested in these stocks.

Though this industry is cyclical, spending on wafer fab equipment/machines has grown secularly over the last 30 years. With AI, this growth has exploded. Unlike memory, these companies are not at risk of commoditization with one another due to the greater complexity of the wafer fabrication process. The real competitive threat comes from China. Chinese toolmakers are taking share at mature nodes, which is one reason our base cases assume WFE companies will lose share there. But as the leading edge is growing so quickly, the net effect will be a gain in share.

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Despite strong share price gains in the last 1 year, these firms have recently corrected 15%-40%. In case of further drawdowns, we will be interested in these companies because:

The key 5 companies in this sector are:

Every GPU and CPU begins as a silicon wafer. The equipment companies are the ones that turn that blank wafer into a working chip, which puts them at the base of the AI industry.

Below, we can see the various steps involved in treating a pure silicon wafer to create the patterns and connections needed for electronics. This is a highly simplified diagram, and the process is not linear as shown below but circular. Each of these steps will be done multiple times (even 100s) to create the GPU or CPU.

Leading edge chips are built in layers printed one on top of the other. This is a very difficult process as the dimensions are a few nanometers wide.

The raw materials for making chips are discs of pure silicon, 300 millimeters across and polished flat.

Deposition
Every layer on the chip is started by coating it with the required materials. It may be a few atoms thick metal layer or some other electrical part needed in the chip.

Lithography: drawing the pattern
The lithography machine uses light to project patterns and define the circuits on the wafer. ASML’s latest machines can cost upwards of $350M per machine. Lithography sets the pace of the fabrication process as it prints the patterns on which other machines coat, carve, etch or finally check the circuits.

Etch: taking material away
This machine removes material from parts where the pattern has not been made. This is exceedingly difficult. For example, cutting a hole straight through 100+ layers without affecting the whole structure.

Cleaning, treating, and polishing
Cleaning is important as even a speck of dust can render the chip useless. Polishing is required to ensure the surface is even, as you can’t print the next layer correctly on an uneven surface.

Metrology and inspection: checking the work

Once the above processes have been performed multiple times and we have a wafer with multiple chips on it, it needs to be checked. This is very important as each wafer can be worth 100s of thousands of dollars at this stage. So, searching for defects is important because it improves the process yield.

Packaging: increasingly important for AI chips
Assembly used to be cheap work. AI changed that because today’s AI chips (GPUs/CPUs) are several chips stacked and wired together. This stacked 3D structure requires expensive packaging. In fact, packaging capacity is a key bottleneck. The spend here goes to smaller companies like BESI. We will not cover them in this article.

The value chain works like this: Hyperscalers (cloud providers and Meta) and model labs order GPUs and CPUs from chipmakers like Nvidia, AVGO, Intel, and AMD. These companies own the intellectual property for chip design but don’t have their own manufacturing capabilities (except Intel).

These companies then place manufacturing orders with TSMC, Samsung, and Intel (mostly placing their own orders). These companies manufacture the wafers and circuits physically. Inside their factories, they require Wafer Fab Equipment (WFE).

The conversion ratio of Semiconductor capital spending to wafer fab equipment spend is roughly ~58%.

The diagram below shows RC estimates of how $1 of capex for wafer fab equipment is allocated across process steps. Lithography is the largest share of spending at 28%. Within Litho, ASML is the large monopoly player. Then, the Etch and Deposition steps account for ~21%-22% of WFE spend. In these 2 steps, Lam Research (LRCX) and Applied Materials (AMAT) are the leading providers, with TEL also having a decent share. Process control is ~11%. The remaining ~18% of the spend covers ion implantation, polishing, cleaning, and coat-and-develop tools.

The market share by company in each step is shown below:

ASML is the monopoly player in Lithography. The 90% share shown above is for the entire lithography space. In cutting-edge equipment, ASML is the sole provider.

Lam Research leads in Etch and is the most exposed to memory companies. AMAT leads in deposition and KLA in the process control step. In fact, KLA holds more than a 50% share in certain leading-edge process control steps.

The key reason for sharing this report with you is that the WFE companies have brilliant business models. They have dominant market shares in critical equipment. As and when these stocks experience larger drawdowns, we will share our full deep dives on them. For now, we will share preliminary valuations for each company.

Equipment spending was ~$18B in 1995 and ~$116B in 2025, using estimates from SEMI (a semiconductor industry body). That’s a nearly 6.5% CAGR over 30 years. The last decade’s CAGR was ~13% as the leading edge has become much more expensive. In 2026, the WFE companies are talking about strong growth exceeding 20%.

Below, we show the RC estimate of WFE spending through 2030 for the bear, base, and bull cases.

We have done the WFE forecasting bottom-up. We took the expected capex from Samsung, TSMC, Micron, SK Hynix, etc., and then forecast it through 2030. Then we applied a conversion ratio to that spend to estimate our WFE. The key difference among the bear, base, and bull scenarios lies in the shape of the capex. In our bull case, we expect AI spending to continue scaling, driving secular growth through 2030. In the bear scenario, we expect 2028 to be the year when WFE spending declines sharply, as manufacturers have overinvested in capacity. Our base case assumes secular growth, but 2029 WFE spend will be flat to marginally lower than 2028.

Historically, this has been a cyclical industry. We expect that to continue. We don’t believe those who say semiconductors have ceased to be cyclical, as they have no data to support it.

The recovery from any decline in WFE spend may not be quick. This is the key bear pointer for us to watch if WFE companies enter a deep drawdown, which could happen if AI growth rates plateau or even just slow from their current high.