Humanoid robots typically contain between 1,000 and 2,000 semiconductor chips, approximately an order of magnitude higher than the content found in a conventional industrial robot. Interestingly, as shipments of humanoids scale from thousands to tens of thousands of units, the corresponding semiconductor total addressable market (TAM) does not expand linearly. Instead, it drives multiplicative growth, shifting the critical decision for chip suppliers away from whether or not to initiate capacity planning, to which quarter to commence the volume ramp-up.
Over the past decade, differentiation amongst robot OEMs has been centered primarily on mechanical precision, payload capacity and system reliability. Semiconductors have predominantly served an enabling function — driving actuators, controlling motion trajectories, and facilitating signal transmission — while remaining largely peripheral to the core value proposition.
Now, the paradigm is shifting. As artificial intelligence transitions from purely digital environments to physical operational settings, robots’ dependence on semiconductors has expanded from basic execution to encompass real-time perception, autonomous decision-making, and adaptive action. Consequently, the semiconductor proportion of total robot system costs is increasing.
For semiconductor vendors assessing opportunities within the robotics sector, the analytical starting points are clear: What is the current revenue opportunity? At what rate is it expanding? Which application segments offer the highest incremental demand?
Without a well-grounded projection of shipment trajectories, however, any quantitative assessment of chip demand ultimately lacks a robust foundation.
From system shipments to chip demand: a mapping framework
Our Semiconductor Components in Robotics Quarterly Tracker offers a systematic approach: integrating robot shipment data with semiconductor component research, and dynamically reflecting TAM evolution through quarterly tracking of four product categories: humanoid, industrial, collaborative, and mobile robots.
The core logic is relatively simple; each robot category has a defined chip composition, and every fluctuation in shipment volume generates a quantifiable ripple effect on semiconductor demand.
Deriving TAM from shipment data requires comprehensive coverage of each link in the value chain. Our research has long tracked the four robot categories mentioned above, with coverage extending beyond OEM shipment data to bill of materials (BOM)-level chip content analysis. This penetrative research framework enables an accurate translation of shipment changes into semiconductor demand shifts.
Consider industrial robots; global shipments grew 4.9% in 2025. For semiconductor suppliers, this translates into proportional growth in shipments of high-precision encoders, IGBT power modules, functional safety MCUs, and other chips. Consequently, semiconductor revenues from industrial robots is projected to reach $1.0 billion by 2031.
Collaborative robots offer a similar perspective; global shipments reached approximately 60,000 units in 2025, up 15.1% year-on-year, directly driving demand for high-precision sensors, functional safety chips, and communication modules. We forecast annual collaborative robot shipments to reach approximately 130,000 units by 2030, meaning semiconductor demand in this segment is expected to more than double within five years.
Humanoid robots: The steepest growth trajectory
Among all robot categories, humanoid robots exhibit the highest semiconductor density and the steepest growth slope.
In 2025, semiconductors accounted for approximately 10% of total humanoid robot BOM. By 2031, that share is expected to double to 21.3%. This is not simply a matter of adding more chips; It reflects robots becoming inherently more capable. From joint-level control to AI inference and from multi-sensor fusion to functional safety redundancy, every capability enhancement pushes up the silicon content per unit.
The structural difference runs deeper; traditional industrial robots concentrate semiconductor demand in power devices and MCUs to execute deterministic programs and motions. However, humanoid robots must operate in open, unstructured environments, generating explosive demand for AI accelerators, high-precision sensors, high-bandwidth communication, and functional safety chips.
In contrast, the semiconductor cost share for industrial, collaborative, and mobile robots shows much flatter growth, rising from about 8% to 8.3%, 10% to 10.8%, and 8% to 9.1%, respectively. This is far less dramatic than the sharp increase expected for humanoid robots, but it is important to keep in mind that these three robot types already have a vastly larger shipment base than humanoid robots. Even though their per-unit semiconductor cost share is not growing by much, their sheer volume means that together they still generate a significant amount of total semiconductor revenue. In other words, they remain the core foundation and main revenue driver of the current robot semiconductor market.
The signal from these flattening curves is fairly clear: the semiconductor technology dividends of traditional robot form factors have been largely absorbed. Architecture is maturing and incremental headroom is becoming more limited, although this does not make traditional markets unimportant. Industrial robots still remain the largest installed base for semiconductors in robotics and AI is injecting new vitality into this segment. Our forecasts indicate that robot-related semiconductor sales will grow approximately 14.6% annually over the next five years, outpacing most other industrial sectors, with concentration particularly in the Asia-Pacific region.
Mobile robots and the value of quarterly tracking
The mobile robot market provides another case for deriving semiconductor demand from shipment data. Mobile robots are growing at an average annual rate of approximately 16%, directly driving demand for simultaneous localization and mapping (SLAM) navigation chips, communication modules, sensors, and power devices.
We forecast mobile robot revenue will grow from $6.2 billion in 2025 to $14.0 billion by 2030. For semiconductor suppliers, this represents a sustained and expanding demand pool,with the demand structure evolving from simple drive control toward the full perception-decision-execution chain.
The value of quarterly tracking lies in capturing high-frequency market signals. The robotics market does not evolve at a uniform pace. Humanoid robots’ tenfold production increase in 2025, collaborative robots’ demand rebound in specific quarters, mobile robots’ regional market fluctuations; these signals can only be identified at quarterly granularity.
When shipment volumes in each robot category show abnormal movement in a particular quarter, the data helps distinguish whether this is a structural trend or a short-term disturbance. And when the BOM structure of a robot category changes, quarterly data enables more timely responsiveness.
Final thoughts
The robotics and semiconductor industries are moving from a loose upstream-downstream relationship toward deep integration. Humanoid robots, as the technological high ground, are driving growth in AI chips and third-generation semiconductors, while traditional robots, empowered by AI, are generating renewed demand vitality.
Deriving semiconductor TAM from robot shipment volumes is not an exercise in estimation. It requires a foundation built on data from every link in the value chain. Our work is to make this mapping process as transparent as possible, transforming it from a black box into an analytical framework, so that semiconductor companies can clearly see how every shipment of every robot type leaves its mark on the chip demand landscape.
Ultimately, a differentiated understanding of chip requirements across robot form factors, and a grasp of the migration pace of value from "execution" to "intelligence," will be central to strategic positioning in the years ahead.
