Applications
Humanoid Robot
As AI reshapes robotics, humanoid robots are driving surging demand for high-performance semiconductors — and ASE's advanced packaging is built to meet it.
Overview
The robotics field is transforming with artificial intelligence (AI), as humanoid robots become a key focus for innovation and investment. According to a recent report, the humanoid robot market is expected to reach USD 180 billion by 2035, fueled by increasing applications in industrial automation, logistics, healthcare, and service environments. From advanced sensors that mimic human five senses to powerful processors capable of real-time decision-making, the demand for high-performance semiconductors is surging. At the heart of this transformation lies the development of advanced AI control chips and integrated hardware systems that enable real-time data processing and decision-making. The design of these components is critical, as they must support high-density computation while managing thermal loads and ensuring signal integrity. Furthermore, the rise of collaborative robots, or cobots, highlights the importance of spatial intelligence through effective multi-modal data synchronization and real-time AI inference, allowing these machines to work safely alongside humans and adapt to their surroundings. ASE's advanced packaging technologies, including 2.5D/3D IC, FOCoS/FOSiP as well as SiP/WLP, are positioned to address these challenges. This article also outlines future opportunities for flexible and stretchable packaging solutions that can support the high degrees of freedom required in humanoid robotics. By leveraging these insights, semiconductor companies can capitalize on the burgeoning humanoid robotics market and drive innovation in this transformative field.
Current trends & innovations in humanoid robots
Revolutionizing humanoid robotics through AI integration
Unlike traditional robots in factories, humanoid robots need to move, sense, and interact with people in a natural and intuitive manner. Achieving this level of interaction involves a sophisticated "brain-cerebellum-limb" collaborative system. The "brain," powered by advanced AI accelerators like NVIDIA Jetson Thor or RTX-class GPUs, handles perception and planning, while the "cerebellum," typically a CPU-based motion controller, manages real-time control of the "limbs," enabling the robot to perform tasks with minimal human intervention. The computing power of both the "brain" and "cerebellum" directly influences the intelligence and application potential of humanoid robots. Key design focuses for AI Control Chips include high-density computation, multi-chip integration using Chiplet or 2.5D/3D packaging architectures, and effective thermal management. Additionally, developing low-latency interconnects and optimizing system-level power consumption are critical priorities.
Integrated hardware for real-time robot control
As humanoid robots become increasingly sophisticated, the need for modular designs that support mass production and simplify maintenance is increasing. Robots are now equipped with a variety of sensors—visual, force, IMUs, and tactile sensors—to effectively gather data and respond to their environment. To perform complex tasks without sacrificing size or weight, integrating more functionality into compact designs is essential. In addition, real-time responses are critical for actions like avoiding obstacles or maintaining balance. This evolution has spurred the widespread adoption of Sensor-in-Package technology, which integrates sensor dies, MEMS, and CMOS processing units into a single, compact package. This integration allows for shorter signal paths and reduced latency. Furthermore, driver ICs have evolved from basic control units to include computation and feedback capabilities, thereby enabling real-time feedback.
Implementing spatial intelligence in collaborative robotics
Collaborative robots, or cobots, are emerging as a pivotal trend in automation due to their ability to work safely alongside humans. Their ease of programming, increased degrees of freedom, and electric-driven design make them highly versatile. However, to fully realize their potential, cobots require advanced spatial intelligence—the capability to interpret and act upon multimodal inputs such as images, videos, text, speech, and real-world sensor data. This evolution requires a close integration of sensors, AI inference chips, and memory through heterogeneous packaging, as system architecture shifts towards distributed, near-sensor inference to facilitate high-performance AI capabilities. Key technical pillars driving this trend include Multi-Modal Data Fusion, which integrates inputs from vision, LiDAR, and tactile sensors for a comprehensive understanding of spatial environments, and Real-Time AI Inference, which allows for immediate decision-making based on diverse sensor data.
Key issues & solutions for OSATs in humanoid robots
Enhancing AI control chips: addressing thermal and signal integrity challenges
To facilitate real-time motion control, AI chips in humanoid robots must deliver high-frequency, low-latency computation. However, the high-density computation involved generates substantial thermal loads that can compromise both performance and longevity. While advanced integration techniques—such as Chiplet, 2.5D, and 3D packaging—address processing needs, they also introduce complex challenges related to signal integrity and thermal management. Incorporating thermal management features such as heat spreaders can significantly improve heat dissipation and overall performance. Additionally, co-design verification and package-level testing are critical for validating signal integrity, thermal performance, and reliability. By addressing these challenges, AI control chips for humanoid robots can achieve the necessary performance standards while ensuring durability in demanding environments.
Mitigating noise and interference in sensors and driver IC integration
To achieve effective real-time control in humanoid robots, it is essential that sensors and driver integrated circuits (ICs) are designed to fit into compact volumes while delivering high-precision measurements and immediate feedback. However, the miniaturization of these components can increase their susceptibility to noise, interference, and manufacturing variability, which poses significant challenges for reliable operation. High-density integration further complicates the packaging process, making reliability and functional testing more critical than ever. Although shorter signal paths can enhance performance, they also become more sensitive, necessitating precise characterization and validation. To address these challenges, employing shielding and isolation designs can help mitigate electromagnetic interference and signal noise. Additionally, implementing module-level and package-level testing is crucial to ensure the accuracy of sensors and the performance of real-time feedback systems.
Enhancing spatial awareness: multi-modal data synchronization and real-time AI inference
Advanced humanoid robots require full spatial awareness to move seamlessly in human environments. This capability relies on a synergy of vision, LiDAR, IMU, and tactile sensors, all powered by real-time AI inference. However, the complexity of synchronizing and fusing multi-modal data presents significant challenges, as any delays or mismatches can compromise control accuracy. To address these challenges, it is essential to employ distributed, near-sensor inference architectures to minimize data transmission load and reduce latency. Furthermore, conducting thorough system-level and scenario-based testing is crucial to validate multi-modal data synchronization, ensure real-time AI inference, and confirm overall system reliability under real-world operating conditions.
ASE technology platform
VIPack™ — 2.5D/3D IC packaging
VIPack™ is a vertically integrated advanced packaging platform developed by ASE, designed to implement 3D heterogeneous integration and 2.5D/3D IC architectures. Supported by the Integrated Design Ecosystem™ (IDE) that spans silicon, package, and system domains, VIPack™ is engineered to meet the next-generation demands for performance, bandwidth, and power efficiency in AI, high-performance computing (HPC), and data-centric applications. 2.5D/3D heterogeneous integration enables multiple dies—such as control system-on-chips (SoCs) and high-bandwidth memory (HBM)—to be co-packaged with high-density horizontal and vertical interconnects, reducing interconnect latency while improving bandwidth and power distribution. ASE's IDE facilitates early simulation and optimization of thermal paths and signal/power integrity across silicon and package, mitigating overheating and performance degradation, reducing iteration cycles, and accelerating time to market.
VIPack™ — FOCoS / FOSiP
Positioned under the ASE VIPack™ platform, FOCoS (Fan-Out Chip on Substrate) and FOSiP (Fan-Out System in Package) are advanced fan-out packaging technologies that enable high-density die-to-die interconnects. These technologies enable the use of shielding and isolation designs to mitigate electromagnetic interference (EMI) and signal noise in sensor and driver IC integration. FOCoS and FOSiP eliminate the need for an interposer/substrate that often acts as an antenna for noise. They allow for the creation of sophisticated Redistribution Layers (RDLs) that enhance signal integrity through integrated ground planes and "guard rings," which act like miniature Faraday cages to trap EMI. In addition, ultra-short interconnects and advanced isolation techniques like trench shielding enable the placement of noisy driver ICs and quiet sensors within the same package, resulting in cleaner, more reliable signals without the need for external shielding.
SiP / WLP
SiP (System in Package) and WLP (Wafer Level Packaging) are advanced packaging methodologies that integrate multiple integrated circuits (ICs), passive components, and subsystems into a single, compact package with minimal footprint. ASE SiP solutions combine heterogeneous dies and passive components into compact modules, while WLP—including Wafer-Level Chip Scale Packaging (WLCSP) and advanced fan-out WLP—builds packages at the wafer level before singulation, resulting in ultra-thin, low parasitic solutions. These packaging technologies address key challenges in sensor and driver IC integration miniaturization and the complexity of multi-modal sensor fusion and system integration. SiP packaging allows for the co-packaging of various functional dies, such as controllers, memory, and sensors, which reduces board complexity and external interconnect delays. WLP solutions offer the smallest form factor with direct wafer-level assembly and short electrical paths, enhancing signal and thermal performance for real-time responsiveness. Together, SiP and WLP facilitate the tight integration of diverse functional blocks necessary for multi-modal perception and control in humanoid robots, leading to reduced latency and improved system reliability.
Conclusion & recommendations
Semiconductor packaging has evolved from a supporting role into a system-level enabler for humanoid robots as the landscape of humanoid robotics is undergoing a remarkable transformation driven by the integration of artificial intelligence. The convergence of AI with humanoid robots is not merely enhancing their capabilities but is fundamentally redefining their role in human environments. The ability of these robots to learn, adapt, and interact meaningfully with humans is a testament to the advancements in AI control chips, integrated hardware, and real-time data processing. The challenges associated with thermal management, signal integrity, and multi-modal data synchronization are being addressed through innovative solutions such as 2.5D/3D packaging and advanced fan-out technologies. These developments are crucial for ensuring that humanoid robots can operate efficiently and reliably in dynamic settings, paving the way for their widespread adoption in various sectors, from healthcare to manufacturing. Looking ahead, the future of humanoid robotics will hinge on the continued evolution of packaging methodologies that accommodate the unique demands of these sophisticated machines. As we push the boundaries of what is possible, the integration of flexible and adaptive materials will be essential to meet the mechanical and electrical performance requirements of next-generation humanoid robots.
