Key takeaways
- AI, mobility and cloud are the growth engines: They’re pushing chips toward a $1 trillion market by 2030 and forcing fabs to invest in sub-3nm nodes and advanced packaging
- Chips are bigger and more complex: Larger dies and heterogeneous packaging need smarter inspection, inline metrology, AI-driven defect classification and faster testing to protect yield
- Digital twins speed everything up: Teams simulate subsystems and process windows, train AI with synthetic data and catch issues earlier, which cuts re-spins, time and cost
- Glocal supply chains reduce risk: Build globally but execute locally: diversify capacity and talent, balance exposure across markets and ramp in step with fab sequencing
- Sustainability is a design requirement: Recycle water and chemicals, optimize recipes and energy, use condition-based maintenance and report lifecycle carbon to win customers
Semiconductors are set for a generational surge. In a recent HCLTech Trends and Insights podcast episode, Biju Nambisan, Senior Vice President and Head of the Semiconductor Equipment Sales Function at HCLTech, explored how demand from AI, mobility and cloud is reshaping AI-enabled semiconductor manufacturing and changing what gets built, how it’s built and where value is generated. He framed a market “estimated to grow to about a $1 trillion market by 2030, up from $650 billion today,” with equipment makers at the heart of the transformation.
Why the semiconductor equipment market is changing rapidly
Nambisan sees multiple forces compounding and accelerating change in the semiconductor equipment market and development industry trends:
- Policy tailwinds: “Investments…including the CHIPS Act and the European Chips Act” are catalyzing local capacity, glocal supply chains, and R&D around the world
- New demand engines: “AI PCs, AI data centers [and] AI in mobility” will expand silicon intensity across devices and infrastructure
- Custom silicon everywhere: “Automotive OEMs want to control their destiny,” and hyperscalers are also “delivering chips by themselves”
- Node and packaging shifts: With an increase in new chip projects are being launched on the very newest manufacturing processes — “sub-3-nanometer” — and “advanced packaging” proliferating, fabs are investing globally
These forces converge in the equipment arena, where capital intensity, R&D velocity and service depth will increasingly differentiate winners.
How sub-3nm chips challenge semiconductor equipment makers
Design complexity is exploding. “In 2020, the supported die size was about 200 mm². In 2025, the same die is about 800-plus mm²,” said Nambisan. Larger die* areas, more transistors and heterogeneous packaging create a step change in chip inspection tools and advanced packaging equipment for metrology and test. He added that “AI is being used across memory, advanced packaging and the number of transistors that get into a die,” enabling smarter defect discovery, tighter process control and faster root-cause analysis, which are all essential as GPUs and other accelerators push architectures forward.
That shift demands:
- Inline metrology to catch excursions early.
- AI-assisted defect classification to separate nuisance from signal.
- Higher-throughput test to safeguard yield learning at ramp.
Equipment roadmaps are responding with edge analytics at the tool, tighter APC loops and richer data plumbing between equipment, fab and design teams.
*A die is a small, individual piece of a silicon wafer that contains a complete integrated circuit (IC).
How digital twins accelerate semiconductor equipment development
Digital simulation isn’t new, but the economics and accuracy have caught up, enabling AI-optimized semiconductor equipment development. “Digital twins at a subsystem level help design efficiency…reduce cost…and aid testing efficiency,” said Nambisan. Previously, proving yield demanded full cyclic runs; now, more accurate twins let teams “simulate much better” and de-risk earlier, shortening time to stable, high-yield production. The payoff is faster validation, fewer costly re-spins and a clearer understanding of how recipe changes ripple across complex tools.
Where twins help most:
- Subsystem design and verification
- Virtual try-outs for process windows
- Training AI models on synthetic but realistic data
- Predictive maintenance scenarios before field release
Glocal supply chains in semiconductor equipment manufacturing
The pandemic and geopolitics exposed brittle single-country dependencies. The answer, said Nambisan, is nuance: “It is no longer local, it is glocal.” What that looks like:
- Capacity in more places: New fabs and localized solutions diversify risk.
- Talent diversity: India plays “a big role…in [semiconductor] talent,” and emerging sites create alternate nodes for logistics and support.
- Multi-market balancing: One equipment customer with “about 35% of revenues coming from China” is “looking at diversifying…so there’s no single source of failure.”
- Realism on pacing: Re-platforming supply chains “takes time”; equipment programs are capital-intensive and sequence dependent.
In short, “it is glocal”: think globally, execute locally and plan for redundancy without fragmenting standards or quality.
Essential skills for semiconductor equipment engineers
From design to testing, AI is becoming a non-negotiable across the semiconductor equipment lifecycle. But Nambisan emphasized deeper fundamentals: “You need advanced skills… background in physics or material science,” because equipment roadmaps depend on new chemistries and substrates.
Leaders should:
- Build cross-disciplinary teams blending AI and ML with device physics
- Invest in materials and plasma expertise
- Train for design-for-inspectability to accelerate learning cycles
- Create rotational programs across fab, supplier and field service to scale tacit knowledge
How partnerships drive semiconductor equipment innovation
No one ships alone. “This is a sector…controlled by IP providers and hyperscalers,” and while there was “aversion to go to cloud initially,” Nambisan now sees “Electronic Design Automation [EDA] on cloud” gaining ground. Success hinges on roadmaps moving in concert:
- Foundry alignment: Equipment and chip teams need “advanced planning cycles with foundries”
- IP and EDA linkages: Access and portability of IP must match tool and cloud choices, a requirement for integrated semiconductor equipment development and AI-driven manufacturing solutions
- Vertical partnerships: Automotive and telecom players doing “most of the silicon development by themselves” still rely on upstream process readiness and downstream OSAT capacity
Key drivers of semiconductor equipment market growth
When asked what powers the jump from $650 billion to $1 trillion, Nambisan pointed to:
- HPE and mobility: Servers, PCs and mobile, amplified by “AI data centers,” plus industrial automation and robotics
- Verticalized silicon: Custom chips for automotive, industrial and consumer use cases
For equipment makers, variability and customization are endemic. “Many…are very custom-built solutions,” he said. You can’t “suddenly…scale up to produce 100,000 systems.” The practical response:
- Embed AI into inspection, metrology and APC to keep pace with die size and complexity.
- Shorten cycle times with digital twins and virtual try-outs.
- Modularize subsystems to reuse qualified designs across customer-specific tools.
- Align capacity ramps to fab sequencing to avoid stranded capital.
Semiconductor sustainability: Designing for efficiency
Semiconductor manufacturing is resource intensive. “This sector uses tons of water…and chemicals,” acknowledged Nambisan. The counterweight is intentional design: “Many companies have a sustainability charter…one of the important areas whenever a new equipment is manufactured.” Practical levers include:
- Water recycling and closed-loop chemistries
- Recipe optimization to cut consumables and energy
- Condition-based maintenance to extend part life and prevent scrap
- Transparent carbon reporting across the tool’s lifecycle
Sustainability is not an add-on; it’s an engineering requirement that increasingly shapes winning equipment.
Why equipment makers are critical to semiconductor industry growth
If chips are the economy’s compute foundation, equipment is the foundation of chips. As AI expands and packaging gets denser, toolmakers will arbitrate the trade-offs among precision, yield, sustainability and cost. Nambisan’s message is clear: invest early, plan collaboratively and build glocal resilience. With AI-infused inspection, digital twins that de-risk earlier and partnerships that synchronize roadmaps, the industry can meet surging demand and do so responsibly. The next decade will reward organizations that scale AI, master advanced packaging and orchestrate resilient, sustainable supply chains; turning complexity into compounding advantage.
FAQS
What drives growth in the semiconductor equipment market?
A convergence of AI PCs, AI data centers and AI in mobility, plus verticalized and custom silicon and policy tailwinds, such as the CHIPS initiatives, are expanding silicon intensity. This pushes fabs to invest in sub-3nm nodes and advanced packaging, lifting demand for leading-edge inspection, metrology, and test equipment.
How can AI improve semiconductor equipment development?
AI powers defect discovery and classification, speeds root-cause analysis and tightens advanced process control (APC). At the tool edge, analytics flag excursions early; across the fab, AI links equipment data with design feedback to protect yield learning during ramps and sustain throughput at ever-larger die sizes.
Why are glocal supply chains important for equipment makers?
Single-country dependencies proved brittle. A glocal model, including diversified capacity, localized support and logistics and distributed talent reduces risk while preserving global standards. It also helps customers rebalance revenue exposure and sequence capital ramps without stranding inventory.
What is the role of digital twins in semiconductor manufacturing?
Modern twins, accurate and economical, let teams simulate subsystems, run virtual process windows, train AI with synthetic yet realistic data and validate predictive maintenance before field release. The result: fewer re-spins, faster validation and shorter paths to stable, high-yield production.
What skills do engineers need for next-gen semiconductor equipment?
Beyond AI/ML fluency, engineers need deep physics and materials science. Cross-disciplinary teams should practice design-for-inspectability and rotate across fab, supplier and field service to scale tacit, system-level knowledge.
How do partnerships affect semiconductor equipment success?
Success depends on roadmap synchronization: early foundry alignment, IP/EDA portability (increasingly on cloud) and vertical collaboration with automotive and telecom players doing custom silicon. These ties ensure process readiness, OSAT capacity and faster bring-up of new tools.
How does sustainability impact semiconductor equipment design?
It shapes requirements from day one: water recycling, closed-loop chemistries, recipe and energy optimization and condition-based maintenance to extend part life and cut scrap. Transparent lifecycle carbon reporting is increasingly expected and can be a competitive differentiator.
How do equipment makers handle larger dies and advanced packaging?
By upgrading the full learning loop: inline metrology to catch excursions early, AI-assisted defect classification to separate signal from noise, higher-throughput test to protect yield during ramp and tighter APC with edge analytics plus richer data plumbing between tool, fab and design. On the design side, modularized subsystems enable reuse of qualified blocks across custom tools, speeding delivery without sacrificing performance.
