The semiconductor manufacturing sector is undergoing rapid growth and transformation, primarily fueled by demands from digital transformation, the Internet of Things (IoT), Artificial Intelligence (AI), 5G, Electric Vehicles (EVs), and High-Performance Computing (HPC). These applications require higher performance, efficiency, and functionality from chips, giving rise to several key trends in semiconductor manufacturing technologies:
According to market projections, global semiconductor demand will continue to grow steadily in the coming years, with the total market size expected to reach approximately USD 1.2 trillion by 2035. In terms of demand distribution, data centers and high-performance computing (HPC) are anticipated to account for the largest market share. Additionally, the electric vehicle (EV) sector and related power components are expected to see a significant surge in demand, positioning them among the fastest-growing application segments. While the growth rate of smart terminal devices—such as smartphones and wearables—may slow down, stable demand will continue to support a substantial shipment volume. At the same time, industrial automation and smart manufacturing will drive increased demand for embedded chips and sensors. This diversified demand landscape highlights the growing breadth of semiconductor manufacturing and underscores the importance for companies to optimize technologies for different application scenarios—in order to meet market challenges and capitalize on growth opportunities.
Semiconductors are the cornerstone of modern technological advancement, with applications spanning a wide range of sectors, including consumer electronics, industrial automation, automotive electronics, communications and networking, artificial intelligence (AI), high-performance computing (HPC), medical technology, and aerospace and defense. From everyday devices, such as smartphones and laptops, to industrial applications like smart manufacturing and machine vision inspection, and even military and defense systems, including high-precision radars and satellite navigation, semiconductor technology is omnipresent, continuously driving global industrial innovation and advancement.
In the consumer electronics sector, semiconductors significantly enhance processing power and energy efficiency, enabling smartphones, tablets, and smart home appliances to achieve faster computing speeds, longer battery life, and superior display performance.
In industrial applications, smart manufacturing is increasingly adopting AIoT (Artificial Intelligence of Things) and machine vision systems to boost production efficiency. Through precision sensors and real-time data analytics, enterprises can achieve high-precision quality inspection and process optimization, reducing manufacturing costs and increasing yield. Advances in energy management technologies—such as smart grids, solar power generation, and power management systems—also benefit from semiconductor innovation, resulting in more efficient operations.
In automotive electronics, the rise of EVs and autonomous driving has driven demand for high-performance processors, AI chips, and sensor modules—facilitating the transformation from traditional internal combustion engines to intelligent and electrified vehicles. The evolution of advanced driver-assistance systems (ADAS) and in-vehicle infotainment platforms has made driving experiences safer, more convenient, and smarter.
In communications and networking, advancements in 5G base stations, Wi-Fi 6/7, optical communication, and satellite communication have drastically improved global data transmission capabilities. These technologies provide faster and more stable network connections, supporting applications such as edge computing, smart cities, and interconnected infrastructure.
The AI and HPC domains have become increasingly dependent on semiconductor innovation. Technologies like AI accelerators, GPUs, NPUs (Neural Processing Units), cloud computing, and edge AI are continuously fueling the growth of data centers and intelligent applications.
The medical technology field is also benefiting from semiconductor advancements. Applications such as medical imaging processing, wearable health devices, and telemedicine solutions are transforming traditional healthcare models, improving diagnostic accuracy and enabling more comprehensive health monitoring.
Last but not least, in aerospace and defense, the evolution of semiconductor technology has propelled the development of satellite navigation, military-grade radar, and unmanned aerial systems (UAS) enhancing national security and precision communication. Whether in precision strike systems, global positioning, or battlefield communications, the high-performance computing power of semiconductors plays a mission-critical role.
The upstream sector includes IC design, semiconductor materials, and equipment. Key materials such as silicon wafers, photomasks, photoresists, etching agents, and packaging substrates have a direct impact on chip yield and performance. Critical manufacturing equipment includes photolithography systems, etching tools, thin-film deposition systems, and ion implanters. IC design is a core process in which companies define the chip architecture, logic, and circuits before sending the designs for fabrication.
Wafer fabrication is the heart of semiconductor manufacturing and consists of complex processes like photolithography, etching, thin-film deposition, ion implantation, CMP (Chemical Mechanical Planarization), and metal interconnect formation. The overall process can be divided into two phases:
After wafer fabrication, chips move to packaging and testing. Packaging protects the chip and ensures electrical connectivity, typically bonding the bare die onto a substrate using wires or Through-Silicon Vias (TSV). Traditional packages include DIP (Dual In-Line Package) and QFP (Quad Flat Package), while advanced methods like Flip-Chip, Fan-Out, and 3D IC enhance performance and integration. Testing includes functional and reliability tests to ensure the chip’s performance across various conditions.
With semiconductor demand surging, the importance of manufacturing precision and efficiency has grown. Integrating edge AI into production scenarios is driving innovation in wafer manufacturing and downstream packaging and testing. Edge AI enhances real-time data processing and decision-making directly at the production site, leading to significant improvements in various manufacturing processes. Below is an edge AI use case in wafer cleaning:
Wafer cleaning is a critical process that directly impacts yield and process stability. By using chemicals and ultrapure water to remove photoresist, organic compounds, metal residues, and particles, the wafer surface is polished and prepared for subsequent steps. Cleaning accounts for roughly one-third of the manufacturing cycle and is crucial for defect reduction and contamination control. After wafers are removed from the FOUP (wafer pod), mechanical arms and algorithms handle precise alignment. However, repositioning can cause delays. To overcome this, manufacturers integrate machine vision with motion control systems. Vision systems detect wafer offset and warpage in real time and provide precise data to guide positioning and cleaning actions. Vision systems also optimize cleaning parameters, avoiding over-processing or wafer damage. This improves precision and efficiency, especially for removing microscopic particles while preserving surface integrity.
Driven by growing market demand, the semiconductor industry is experiencing sustained expansion. As advanced processes and packaging technologies continue to evolve, these advancements not only enhance chip performance but also enable practical AI implementation. The industry is now at a critical juncture where technological innovation and market needs are simultaneously pushing boundaries. Edge AI offers a transformative opportunity to enhance efficiency and quality in the following downstream semiconductor manufacturing processes:
Die probing and sorting are vital for quality control and manufacturing efficiency. These processes identify defective chips, grade quality levels, and allow manufacturers to perform defect repairs. By doing so, they lower costs, improve yield, and maintain competitive advantages. Advanced die sorters integrate machine vision, AI, and edge computing with multi-axis motion control systems. The vision system evaluates chip surface quality, position, and dimensions, transmitting real-time data to the motion system for precise control during pickup, rotation, classification, and placement. The motion system ensures chip stability during high-speed movements, reducing misalignment and vibration-induced issues. These integrations shorten inspection cycles, minimize human error, and enhance production reliability.
Edge AI systems provide precise and efficient inspection during wafer dicing. With Edge AI, automated correction boosts accuracy and reduces reliance on manual labor. In a typical setup, an industrial camera captures the wafer’s dicing location under auxiliary lighting. The images are sent to an edge AI system via a network switch. The AI analyzes alignment accuracy; if misalignment is detected, the system triggers auto-calibration commands to adjust the dicing blade.
Die bonding is a critical step in IC packaging that directly impacts chip performance and reliability. A die bonder must precisely pick and place dies from a wafer onto the substrate using epoxy adhesives. High-speed, micron-level accuracy is essential to ensure electrical and thermal integrity.
Traditional optical alignment systems struggle to meet modern precision demands. High-performance die bonders now integrate motion and machine vision systems for real-time detection and dynamic compensation.
The vision system determines the chip position and angle, calculating the relative placement of the chip to the substrate. Compensation data is fed to the motion system, which dynamically adjusts its actions to prevent cumulative deviation, maintaining sub-micron placement accuracy.