“Automakers must strategically combine AI and semiconductor technologies to secure sustainable competitiveness.”

Omdia recently released a report titled, “The Direction of Change Led by Semiconductors in the Great Transition of the Automotive Industry.”

Accordingly, as the automobile industry enters an era of transition from internal combustion engine vehicles (ICEVs) to electric vehicles (EVs), semiconductor technology is positioned as a core element of vehicles.

These changes are not limited to simply changing the way vehicles are driven, but are also leading to a convergence with cutting-edge technologies such as digital technology, AI, edge computing, and generative AI, reorganizing automobile manufacturing and the entire value chain.

Automotive semiconductors have been used in the automotive industry since the 1970s, initially in fuel injection systems to reduce exhaust emissions.

Since then, it has continued to develop and has become a key component used in various fields such as body, chassis, safety functions, infotainment, advanced driver assistance systems (ADAS), powertrain, and electric vehicle specialized technologies.

Especially in high-end vehicles, up to 15One or more electronic control units (ECUs) operate based on semiconductors, and processors, memory, sensors, and analog ICs support the smooth operation of vehicle systems.

Electric vehicles require battery-based power systems, and power semiconductors play an important role in this process.

Power semiconductors are components that perform power conversion and control functions and are essential in major systems such as inverters, on-board chargers (OBCs), DC-DC converters, and battery management systems (BMS).

The inverter converts the battery's direct current (DC) to alternating current (AC) to drive the motor, and the onboard charger (OBC) adjusts the external charging power to suit the battery.

The DC-DC converter regulates power between the auxiliary battery and the drive battery in the vehicle, and the battery management system (BMS) monitors and manages the battery condition.

As traditional silicon (Si)-based semiconductors reach their limits, silicon carbide (SiC) and gallium nitride (GaN) are attracting attention as next-generation materials.

SiC offers outstanding performance in terms of fast charging, extended driving range, and system weight reduction, and is being actively adopted by major automakers such as Tesla, BYD, and Hyundai Motors.

GaN is emerging as a next-generation solution that is advantageous for miniaturizing electrical components by providing high-speed switching capabilities and heat reduction effects.

In this context, data quality and availability are key to successfully applying AI in the manufacturing industry, but in automobile factories, equipment not connected to the network, heterogeneous hardware, and format differences between systems are acting as obstacles to AI adoption.

According to Omdia's 2024 survey, 52% of manufacturers cited data silos as a major barrier to AI adoption, with data quality and Data security was also pointed out as a major issue.

Edge computing technology is emerging as an important technology to solve these problems.

To effectively apply AI, technology to integrate heterogeneous manufacturing systems is needed, and edge computing is emerging as a solution.

Analyzing and processing data directly at the manufacturing site through edge devices makes it easier to apply AI within the factory, and can lead to effects such as quality monitoring, predictive maintenance, and improved line efficiency.

According to Omdia research, more than 40% of manufacturers plan to expand the adoption of edge devices in the next 18 months, and the emergence of industrial AI PCs is also expected to contribute to innovation in the manufacturing environment.

Among AI technologies, generative AI is showing potential for wide-ranging use across vehicle manufacturing, operation, design, and customer experience.

In particular, it integrates and analyzes various data such as images, videos, codes, texts, and CAD files through multimodality technology, and supports automation of new vehicle development and maintenance documents, and design of customized functions.

In addition, AI technology is expected to be incorporated into in-vehicle user experiences such as automotive infotainment systems, digital cockpits, and voice interfaces, and generative AI is also expected to be linked to the direction of evolution of automotive semiconductors.

Applying AI to manufacturing processes and semiconductor design requires a strategic approach that goes beyond simple technology introduction, including building an ecosystem, ensuring scalability, and managing data quality.


First, in order to introduce scalable AI, it is necessary to secure a flexible technology infrastructure that can connect vehicle factories and semiconductor design sites, such as cloud interfaces, industrial IoT (IIoT), and edge computing, as well as resolve data silos, secure standardized data formats, and have a high-quality real-time data collection and preprocessing system.

In addition, in order to create sustainable AI value in the introduction of AI for vehicles, a design that considers long-term maintenance and expandability rather than short-term performance improvements is required, and a comprehensive design strategy that reflects data flow and user perspectives is needed.

Effective integration of automotive semiconductors and AI requires an approach that goes beyond simple technology introduction and encompasses the entire complex industrial ecosystem.

To achieve this, elements such as ▲standardization of IoT-based devices and edge devices ▲design of cloud-on-premise hybrid infrastructure ▲development of a collaboration system between semiconductor, software, and automobile OEMs are required.

Omdia said, “AI is emerging as a key factor that is changing the paradigm of the automobile industry beyond simple technological innovation,” and “Automakers must strategically combine AI and semiconductor technology to secure sustainable competitiveness.”