Semiconductor Equipment Localization: How AI Accelerates the Catch-Up
1. The Current Landscape: How Far Domestic Equipment Has Come
The numbers tell a story of acceleration. According to industry data, total revenue from China’s publicly listed front-end equipment companies grew over 37% year-on-year in 2025. NAURA has entered the global top five, and AMEC has reached the global top fifteen. Five years ago, these figures would have been unimaginable.
But the picture varies enormously across segments. Etch and wet clean have progressed fastest, with domestic adoption rates breaking into double digits. AMEC’s plasma etch tools now cover processes at 5nm and below, and ACM Research has been steadily building its reputation in cleaning equipment. In CVD/PVD thin film deposition, Piotech’s PECVD systems support over a hundred process applications, and NAURA’s ALD vertical furnaces have entered volume sales.
However, in lithography and metrology/inspection, the gap remains significant. SMEE has made progress on 28nm DUV lithography, but compared to ASML’s EUV systems, this is not a competition on the same playing field. In metrology, KLA maintains a near-monopoly, and domestic vendors’ market share remains very limited.
My observation is that domestic equipment has moved past the “zero to one” stage, but is now facing the harder challenge of going from “functional” to “production-worthy.”
2. The Underestimated Weakness: It Is Not Just About Hardware
Industry discussions on domestic equipment substitution tend to focus on mechanical precision, RF sources, and vacuum systems — the hardware specifications. These are obviously important, but I believe one dimension is severely underestimated: the maturity of equipment software and process algorithms.
Consider an example. An imported etch tool has a recipe development system refined through over twenty years of iteration, with an enormous accumulated process database. When an engineer develops a new recipe, the system can recommend parameter ranges and flag which parameter combinations are likely to cause problems. Domestic equipment has almost nothing comparable — hardware capabilities are there, but the software-level process knowledge base has not been built yet.
Another example is process control precision. The APC (Advanced Process Control) systems on imported equipment have been refined over many years, with very mature Run-to-Run control models. Domestic equipment in this area generally remains at the SPC (Statistical Process Control) stage, with gaps in both real-time responsiveness and control precision.
These software and algorithm challenges cannot be rapidly overcome through focused hardware campaigns the way hardware issues can. Accumulating process experience fundamentally requires time — but the window of opportunity for domestic equipment is not wide.
3. What AI Compensates For: Using Data to Offset Experience Gaps
This is where AI offers the most valuable contribution to the domestic equipment catch-up: using data-driven methods to partially compensate for the deficit in accumulated process experience.
This is not an idle claim. One of the core competitive moats of established equipment vendors is the process data and experience models they have accumulated across hundreds of production lines worldwide. That kind of accumulation cannot be replicated in three to five years. But AI offers a different path: rather than trying to replicate twenty years of someone else’s experience, machine learning methods can more efficiently extract process insights from limited data.
Specifically, Bayesian optimization can converge on optimal process parameters with very few experimental runs. Transfer learning can port process knowledge gained on one tool to a similar tool, reducing the cost of repeated validation. And reinforcement learning enables control systems to continuously self-optimize during operation, eliminating the need for constant manual tuning.
Of course, AI is not a panacea. It cannot substitute for deep understanding of physical mechanisms, nor can it replace critical hardware engineering breakthroughs. But at the stage of “already able to build it, now needing to build it better,” AI can significantly compress the timeline for catching up.
4. The Equipment Qualification Bottleneck: How AI Shortens Bring-Up Cycles
Anyone who has sold equipment knows that the most painful part of getting domestic equipment into a fab is not signing the contract — it is customer acceptance. An imported tool, backed by a mature recipe library and standardized bring-up procedures, might complete qualification in two to three months. The same process requirements on a domestic tool can easily stretch to six months or longer.
This gap directly impacts customer purchasing decisions. Wafer fabs plan capacity on a monthly basis; an equipment qualification cycle that runs three months longer means the entire line’s production timeline gets pushed back.
The Value of Smart DOE
Traditional equipment bring-up relies on DOE (Design of Experiments), where engineers set parameter ranges based on experience and run full-factorial or fractional-factorial experiments. A single round might consume dozens or even over a hundred test wafers. AI-driven Smart DOE uses adaptive experimental design, dynamically adjusting the parameter direction for the next round based on each round’s results, achieving equivalent or better bring-up outcomes with only 20% of the test wafers required by traditional methods.
For domestic equipment, this is highly significant. Shorter bring-up cycles mean customers are more willing to give trial opportunities; lower test wafer consumption means reduced qualification costs and less customer resistance.
5. Online AI: Closing the Control Precision Gap
Once equipment enters the production line, the real competition begins. What customers ultimately care about is yield and stability, which depend on the equipment’s online process control capabilities.
The Practical Value of Virtual Metrology (VM)
Low domestic adoption of metrology equipment is a reality. But consider an alternative perspective: if the process equipment itself can use sensor data and machine learning models to predict critical process parameters in real time — that is, virtual metrology — then dependence on standalone metrology tools is reduced. This does not mean physical metrology can be entirely replaced, but between two physical measurements, VM can provide continuous process state monitoring, effectively adding a safety net to process control.
For domestic equipment, integrating a high-quality VM module at the factory level could actually become a differentiating advantage. VM solutions for imported equipment are often retrofitted, require additional licensing fees, and do not always interface smoothly with the equipment’s native data bus. Domestic equipment that incorporates AI-enabled control from the design phase has a genuine latecomer advantage.
Upgrading Run-to-Run Control
Traditional R2R control is based on linear models and responds to process drift somewhat rigidly. Machine learning-based R2R control can capture nonlinear process drift patterns and intervene with corrections in the early stages of deviation. In practical terms, AI-enhanced R2R can reduce critical dimension variation by 30% to 50% compared to conventional approaches.
The impact on domestic equipment competitiveness is very direct. Process stability is a core metric in how fabs evaluate equipment. If domestic tools can achieve process consistency approaching that of imported equipment through AI-powered control, customer willingness to substitute increases substantially.
6. The Efficiency Revolution on the Design Side
Another easily overlooked element is the R&D design process for the equipment itself. Domestic semiconductor equipment companies face a practical reality: experienced equipment design engineers are extremely scarce. Established vendors have decades of design heritage and well-developed standard component libraries; a new project can extensively reuse existing design modules. Domestic companies often need to design many components from scratch, leading to markedly higher R&D cycles and costs.
AI-assisted design tools can help by structuring and making reusable the existing design knowledge. Specifically, by learning from historical assembly models, AI can automatically recommend matching standard components and generate 3D assembly layouts after an engineer completes the P&ID design, compressing what previously took weeks of detailed design work into a matter of days.
This is not about replacing an engineer’s creative work, but about automating repetitive design labor so that scarce senior engineering resources can focus on the areas that truly require innovation.
7. Our Perspective: Building the AI Infrastructure for Domestic Equipment
Having covered all this ground, let me share MST Semiconductor’s own positioning.
We do not build semiconductor equipment ourselves — that is not within our competency. What we do is provide AI tools and algorithm platforms for domestic equipment manufacturers and the wafer fabs that use domestic equipment. From AI-assisted design in the R&D phase, to Smart DOE during bring-up and delivery, to online VM/R2R control in volume production — we aim to offer a complete AI toolchain that helps domestic equipment close the gap in software and algorithms as quickly as possible.
Frankly, this is not an easy path. AI applications in semiconductor manufacturing are fundamentally different from internet applications: data volumes are small, precision requirements are extreme, and the cost of errors is prohibitive. We need to truly understand the process, rather than simply applying generic machine learning solutions. But we believe this work is worth doing — domestic equipment hardware capabilities have reached a tipping point, and what is most needed now is precisely the acceleration that software and intelligence can provide.
Domestic substitution should not be a slogan — it should be an engineering problem. AI is not magic, but it is a practical engineering tool. Used well, it can meaningfully compress the timeline for domestic equipment to close the gap.
From AI-assisted design to smart bring-up, from virtual metrology to online process control, MST Semiconductor provides end-to-end AI solutions for semiconductor equipment manufacturers and wafer fabs.
