Semiconductor Market for Robots by Component (Compute, Sensors, Memory, Power Management ICs), Robot Type (Industrial Robots, Professional Service Robots, Personal & Household Service Robots, Drones), Vertical – Global Forecast to 2030
The semiconductor market for robots is expected to grow significantly from USD 11.23 billion in 2025 to USD 41.24 billion by 2030, at a CAGR of 29.7%. Increasing automation across industries is expected to drive the demand for advanced compute, sensing, and power-efficient chips, as robots in manufacturing, logistics, and healthcare require reliable real-time processing and precision control. At the same time, the surge in AI and edge computing will drive the demand for high-performance, application-specific semiconductors that enable local data processing and real-time decision-making, ensuring smarter, safer, and more adaptable robotic systems across diverse environments. These factors are significantly increasing semiconductor demand in robotics.
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Artificial Intelligence (AI) and Machine Learning (ML) are reshaping semiconductor demand across various robotic components, particularly within compute, sensors, and memory domains. AI workloads in robots require powerful yet energy-efficient compute units such as CPUs, GPUs, FPGAs, ASICs, and DSPs, all of which are being optimized to handle parallel processing, real-time analytics, and inference tasks at the edge. For instance, AI accelerators integrated into SoCs (under “Others”) allow robots to process sensor data (vision, motion, or environment) with low latency, making autonomous operation possible even in connectivity-constrained or high-risk environments like warehouses, hospitals, or outdoor terrains.
ML relies heavily on continuous data collection and feedback from a range of robotic sensors — including image, LiDAR, IMU, pressure, ultrasonic, and tactile sensors — to enable adaptive learning and decision-making. These sensors feed data into AI models, which improve robot behaviors such as obstacle avoidance, path planning, and object recognition. To support these ML operations, memory and PMICs (Power Management ICs) become critical for managing high-throughput data access and efficient power delivery across compute-intensive tasks.
As a result, AI/ML integration is driving a semiconductor trend toward domain-specific architectures with tight integration of compute, memory, sensors, and power management, supporting smarter, faster, and more autonomous robotic systems.
Professional service robots are advanced machines designed to operate outside of traditional manufacturing environments, automating tasks in professional sectors such as healthcare, logistics, hospitality, agriculture, construction, and defense. Unlike industrial robots focused on repetitive production line tasks, professional service robots vary widely in form and function, ranging from humanoids and delivery robots to surgical and agricultural robots. They are primarily used to perform dangerous, time-consuming, or labor-intensive jobs, allowing human workers to focus on more strategic or cognitive responsibilities. The market for professional service robots has gained strong momentum in recent years, driven by their benefits in safety, efficiency, and productivity. They enhance workplace safety by performing hazardous tasks, such as demolition or military operations, in place of humans. Their efficiency stems from high uptime, minimal maintenance, and cost-effective performance in areas like inspection, cleaning, and logistics. Additionally, they contribute significantly to productivity through real-time data collection and analytics. For instance, in agriculture, robots are used to monitor crop health. With growing demand for automation across industries, professional service robots are poised to play a vital role in reshaping modern workflows and enabling smarter, safer, and more efficient operations outside of traditional factory settings.
North America is estimated to account for a major share of the semiconductor industry for robots due to its leadership in AI innovation, robotics R&D, and edge computing. The US, in particular, is home to some of the world’s most advanced chip design companies (e.g., NVIDIA Corporation, Intel Corporation, Advanced Micro Devices, Inc., Qualcomm Technologies, Inc.) and robotics pioneers across sectors like defense, autonomous vehicles, logistics automation, and personal robotics.
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The surge in demand for autonomous mobile robots (AMRs), drones, and AI-powered service robots, especially from e-commerce, healthcare, and defense, is driving high-value semiconductor consumption. Furthermore, strategic policy interventions such as the CHIPS and Science Act are leading to heavy investments in domestic semiconductor manufacturing, AI hardware startups, and industrial robotics infrastructure. These developments will increase domestic chip availability and accelerate the integration of advanced semiconductors into robotic platforms. In parallel, the growing adoption of 5G, edge computing, and neuromorphic AI technologies is encouraging deeper semiconductor integration in both mobile and collaborative robots. The convergence of these factors positions North America as the fastest-growing market for robotic semiconductors, with a strong focus on intelligent, connected, and autonomous systems.
Key companies operating in the semiconductor companies for robots are Texas Instruments Incorporated (US), Infineon Technologies AG (Germany), NXP Semiconductors (Netherlands), STMicroelectronics (Switzerland), Sony Group Corporation (Japan), NVIDIA Corporation (US), Intel Corporation (US), Advanced Micro Devices, Inc. (US), Hesai Group (China), and Bosch Sensortec GmbH (Germany), among others.
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