The Complete Guide to AI in Semiconductor Manufacturing: Smart Manufacturing & Yield Prediction

1. Why the Semiconductor Industry Is the Ultimate Testing Ground for AI

Semiconductor manufacturing is one of the most complex production activities in human industrial history. A single advanced-node wafer undergoes over 1,000 process steps from silicon wafer cutting to finished chip, taking 2–3 months, with process parameter tolerances at the nanometer scale. This extreme complexity and precision makes semiconductor manufacturing a natural proving ground for AI technology—traditional statistical methods and human experts' cognitive capacity can no longer cope with such high-dimensional, nonlinear process control challenges.

In their comprehensive review in Expert Systems with Applications, Kang and Cho^[1] systematically mapped the machine learning application landscape in semiconductor manufacturing. They noted that semiconductor manufacturing data characteristics are naturally suited for AI—each fab generates tens of terabytes daily, encompassing equipment sensor data, process parameter records, metrology results, and defect images across multiple modalities. However, this massive data was traditionally used only for post-hoc statistical analysis and compliance documentation, with its potential for real-time prediction and intelligent decision-making largely untapped.

Taiwan's position in the global semiconductor supply chain is irreplaceable. TSMC commands over 60% of global wafer foundry market share, ASE Technology Holding is the world's largest OSAT provider, and MediaTek is the world's fourth-largest IC design company. SEMI's World Fab Forecast report^[5] shows that Taiwan continues to expand capacity at both the advanced and mature process nodes, with AI being the critical lever for ensuring these massive investments translate into maximum output efficiency.

In their research in the Journal of Intelligent Manufacturing, Moyne and Iskandar^[6] further clarified the role of big data analytics in smart manufacturing. They proposed that semiconductor AI applications can be divided into three layers: the first is "descriptive analytics"—understanding what happened (such as SPC control charts); the second is "predictive analytics"—foreseeing what might happen (such as yield prediction, equipment failure warnings); the third is "prescriptive analytics"—determining what should be done (such as automatic process parameter adjustment, scheduling optimization). Most fabs are currently transitioning between the first and second layers, while true competitive advantage comes from achieving the third layer.

Core Drivers of Semiconductor AI

The forces driving the semiconductor industry to embrace AI come from three directions. First, exponentially increasing process complexity: As process nodes advance from 28nm to 3nm and even 2nm, the number of variables affecting yield has exploded from hundreds to thousands, with increasingly complex interaction effects. The efficiency of traditional design of experiments (DOE) and statistical process control (SPC) declines sharply in such high-dimensional spaces^[2]. Second, the harsh economics of yield: An advanced-node fab costs over $20 billion to build, with daily operating costs in the tens of millions of dollars. Each 1% yield improvement corresponds to annualized revenue gains of hundreds of millions of dollars—making any technology that accelerates yield ramp extremely commercially valuable. Third, intensifying global competition: Driven by geopolitical semiconductor self-sufficiency trends, Samsung in Korea, Intel in the US, and SMIC in China are all increasing investments, with AI-driven manufacturing efficiency becoming the invisible battlefield determining competitive outcomes.

2. Wafer Defect Inspection: From SPC to Deep Learning

Wafer defect inspection is one of the earliest and most mature AI application scenarios in semiconductor manufacturing. Traditional defect inspection relied on optical inspection equipment (such as KLA's dark-field inspection systems) combined with statistical classification rules, where process engineers manually determined defect types and root causes based on wafer map spatial distribution patterns. This approach worked well at 45nm and above mature processes but showed increasing limitations as processes shrank and defect patterns grew more complex.

Nakazawa and Kulkarni's research in IEEE Transactions on Semiconductor Manufacturing^[4] is a representative work applying deep learning to wafer map defect classification. They used convolutional neural networks (CNNs) to automatically classify spatial defect patterns on wafer maps—including Center, Edge-Loc, Scratch, Random, Donut, and other common patterns. Compared to traditional rule-based and handcrafted-feature classification methods, CNN models can automatically learn discriminative features from raw wafer map images, achieving over 98% classification accuracy on test sets.

From Defect Detection to Defect Prevention

Chien et al.'s research in the Flexible Services and Manufacturing Journal^[2] advanced AI's role from passive defect detection to proactive defect prevention. Their Fault Detection and Classification (FDC) framework uses real-time equipment sensor data streams to predict during the process whether wafers might develop defects. This represents a fundamental paradigm shift—from "inspecting defects after process completion" to "preventing defects during the process." When the FDC system detects equipment state drift, it can issue alerts within seconds or even automatically pause the process, preventing entire batches of wafers from being scrapped.

In TSMC's practical scenarios, deploying wafer defect inspection AI systems faces several unique challenges. First is the long-tail distribution of defect types—the 5–8 most common defect patterns account for over 90% of total defects, but the remaining 10% of rare defect patterns may have the greatest yield impact. This means models must not only excel at common categories but also maintain high sensitivity to rare categories. Chen's research^[8] points out that combining transfer learning with few-shot learning can effectively identify rare defects with limited labeled data. Second is cross-process model generalization—defect patterns differ significantly across products and process nodes, and training dedicated models for each product is prohibitively expensive. Domain adaptation techniques offer a viable path, enabling models to quickly transfer from one process to new processes.

From a system architecture perspective, Kang and Cho^[1] recommend a layered architecture for wafer defect inspection AI systems: the bottom layer is raw defect coordinates and images from optical inspection equipment, the middle layer is a CNN-based feature extraction and classification engine, and the top layer is a decision interface integrated with MES (Manufacturing Execution System), responsible for translating classification results into process adjustment recommendations or quality disposition decisions. Data latency between these three layers must be kept within minutes to achieve real-time prevention.

3. Virtual Metrology: Real-Time Measurement Prediction

Metrology is the cornerstone of semiconductor manufacturing quality control—precisely measuring critical dimensions and thin-film properties at each process step through metrology equipment (such as CD-SEM, ellipsometers, film thickness gauges) to ensure process outputs meet specifications. However, physical metrology faces two major bottlenecks: first, metrology equipment is extremely expensive with limited throughput, with typically only 2–5 wafers per lot being sampled (less than 10% sampling rate), making it impossible to comprehensively monitor every wafer's quality; second, the metrology step becomes a cycle time bottleneck—waiting for metrology results can take hours, delaying the timeliness of quality feedback.

Su et al.'s pioneering research in IEEE Transactions on Semiconductor Manufacturing^[3] systematically compared the accuracy and real-time performance of multiple Virtual Metrology (VM) algorithms. The core idea of Virtual Metrology is: using hundreds of process parameters collected in real time by equipment sensors during the process (such as gas flow, chamber pressure, RF power, temperature distribution) to build machine learning models that predict process output quality parameters, thereby substituting "virtual" measurements for some physical measurements. Their research found that neural network models could achieve results highly consistent with physical metrology in predicting critical dimensions for etch processes, with prediction errors controlled within acceptable engineering specification ranges.

Three Major VM Application Modes

In practical Taiwanese fab operations, Virtual Metrology has three main application modes. First, WAT (Wafer Acceptance Test) prediction: After wafers complete all process steps but before electrical testing, VM models can predict wafer electrical parameter distributions based on sensor data from each process step, proactively identifying potentially nonconforming wafers so quality engineers prioritize high-risk lots. Moyne and Iskandar^[6] emphasize that this predictive quality management can shorten quality feedback cycles from days to hours.

Second, Run-to-Run (R2R) process control: VM predicted values are fed back to Advanced Process Control (APC) systems to dynamically adjust process parameters for the next wafer batch. For example, when the VM model predicts the current batch's etch depth is trending high, the system automatically fine-tunes the next batch's etch time. This forms a closed-loop control system—VM predictions replace the delay of waiting for physical measurements, improving process adjustment response speed by an order of magnitude^[3].

Third, complete measurement replacement: For process stations with sufficiently high VM model maturity, prediction accuracy is sufficient to completely replace physical metrology, requiring only periodic calibration. This directly frees up metrology equipment capacity, or allows redeployment to advanced process stations with higher physical metrology needs. Lee et al.'s Industrial AI^[7] notes that successful VM deployments typically reduce physical metrology needs by 50–70% while shortening overall cycle time by 5–10%.

VM's technical challenges mainly center on two aspects. Concept drift is the primary challenge—semiconductor equipment conditions change slowly over time (such as chamber corrosion, target consumption), causing models trained on historical data to lose accuracy within weeks. Countermeasures include sliding window retraining, online learning, and model reset mechanisms tied to equipment maintenance events. Cross-tool generalization is another practical pain point—nominally identical equipment exhibits micro-level individual differences (machine-to-machine variation), and maintaining independent models for each tool is prohibitively expensive, while sharing models across tools may sacrifice accuracy. Kang and Cho^[1] recommend a "global model + tool offset correction" hybrid strategy, balancing generalization and accuracy.

4. Yield Prediction and Root Cause Analysis

Yield is the ultimate performance metric in semiconductor manufacturing. The percentage of die on a 300mm wafer that ultimately passes electrical testing directly determines product unit cost and fab profitability. During the introduction of advanced processes (such as 5nm, 3nm), yield often starts below 50%, requiring months or even over a year of yield ramp to reach production levels (typically above 90%). During this ramp, every day of acceleration corresponds to economic value in the millions of dollars.

Chen's research in IEEE Access^[8] explored in depth the role of AI in yield improvement for advanced processes. He pointed out that the core challenge of yield prediction is the "curse of dimensionality"—a wafer's quality is jointly influenced by over a thousand process steps, each involving dozens of process parameters, forming an extremely high-dimensional parameter space with complex interaction effects. Traditional statistical analysis methods (such as principal component analysis, stepwise regression) in such high-dimensional spaces tend to miss important nonlinear interaction terms, while deep learning models—particularly gradient boosting trees (XGBoost/LightGBM) and deep neural networks—can automatically capture these complex interaction patterns.

Root Cause Analysis: From Correlation to Causation

The value of yield prediction lies not only in foreknowing results but in identifying the root causes of yield loss. Chien et al.^[2] proposed an integrated analysis framework combining FDC data with yield data—when the yield prediction model identifies a batch with expected below-target yield, the system automatically traces back to FDC data from each process station for that batch, pinpointing the most likely anomalous stations and parameters. This compresses what traditionally required days of engineer investigation into hours or even minutes.

In practical operations at TSMC, UMC, and other Taiwanese foundries, the AI system for yield root cause analysis typically employs the following technology combinations. Feature importance analysis: Using explainable AI tools (SHAP—SHapley Additive exPlanations) or LIME to extract the top N process parameters with the greatest yield impact from prediction models, providing engineers with clear investigation directions. Anomaly detection: Using autoencoders or isolation forests to identify abnormal operating states in high-dimensional equipment data, even when the anomaly has not yet directly caused yield drops. Moyne and Iskandar^[6] call this "preventive quality control"—intercepting problems before they cause substantive damage. Temporal causal analysis: Combining Granger Causality or Transfer Entropy and other temporal causal inference methods to distinguish true causal relationships from statistically spurious correlations, preventing engineers from being misled by misleading correlations.

It is worth noting that AI-driven yield analysis is not meant to replace process engineers' professional judgment but rather serves as an "intelligent amplifier"—rapidly narrowing the search space within massive data so engineers can focus their limited time and energy on the most probable root causes. Lee et al.^[7] in their Industrial AI discourse particularly emphasized the importance of "human-AI collaboration"—the best yield improvement results often come from combining AI's data processing capability with engineers' physical intuition. Process engineers in Taiwanese fabs possess deep physics and chemistry expertise, and their domain knowledge is irreplaceable by AI models. The real challenge lies in building a workflow that enables engineers to naturally interact with AI tools.

5. AI Optimization for Advanced Packaging and Heterogeneous Integration

As Moore's Law approaches physical limits, advanced packaging and heterogeneous integration have become critical pathways for continued semiconductor performance improvement. TSMC's CoWoS (Chip on Wafer on Substrate), InFO (Integrated Fan-Out), and SoIC (System on Integrated Chips) platforms, along with ASE Technology's FOCoS and VIPack technologies, are all elevating packaging from a mere "chip protection" role to a critical function for "system performance integration." AI plays an increasingly important role in this new arena.

AI challenges in advanced packaging processes differ significantly from front-end wafer manufacturing. First, advanced packaging involves integrating multiple heterogeneous materials (silicon die, organic substrates, metal bumps, underfill adhesives, etc.), where CTE (coefficient of thermal expansion) mismatch and stress accumulation between materials are the primary reliability threats. Kang and Cho^[1] note that machine learning models can learn complex material interaction patterns from thermal cycling tests and warpage measurements to predict long-term reliability risks. Second, alignment precision requirements for TSVs (through-silicon vias) and micro-bumps in 2.5D/3D packaging are extremely high—misalignment exceeding a few micrometers can cause electrical connection failure. Computer vision technology is used for automatic alignment quality monitoring, ensuring each die placement meets precision specifications.

AI Detection of Packaging Defects

On ASE's OSAT production lines, AI visual inspection systems are being deployed at scale for the following scenarios. Wire bonding quality inspection: Checking gold or copper wire loop shapes, bond areas, and pull strength, with AI models determining bond quality within milliseconds, replacing traditional manual sampling inspection. Underfill integrity inspection: Using X-ray images or ultrasonic scan images, AI models automatically identify internal defects such as voids and delamination. Warpage prediction: Based on process temperature profiles, material properties, and packaging geometry, AI models predict warpage degree before packaging completion, allowing engineers to preemptively adjust process parameters to reduce warpage risk.

The CNN architecture from Nakazawa and Kulkarni^[4] that proved successful in wafer map classification has also been transferred to packaging defect inspection. However, characteristics of packaging images—multi-layer structures, 3D geometry, different image contrast from multiple materials—require more complex model architectures. 3D-CNNs, graph neural networks (GNNs), and point cloud analysis are being explored for processing three-dimensional defect information in advanced packaging. Chen^[8] also observed that data labeling costs are particularly high in advanced packaging scenarios (requiring engineers with packaging expertise to interpret X-ray images one by one), making semi-supervised learning and active learning strategies especially valuable.

6. Predictive Maintenance (PdM) for Equipment

Semiconductor equipment maintenance strategy directly affects fab-wide capacity utilization and operating costs. A single EUV lithography machine costs over $300 million, with hourly idle costs in the tens of thousands of dollars; an unexpected CVD (chemical vapor deposition) chamber breakdown can scrap the entire batch of wafers being processed, with losses ranging from millions of dollars to impacts on customer delivery schedules and reputation. In such a high-risk environment, shifting from "fix when broken" reactive maintenance to "precisely foresee" manufacturing AI applications offers enormous commercial value.

Lee et al.'s Industrial AI^[7] proposed a comprehensive framework for semiconductor equipment predictive maintenance. The core logic is leveraging sensor data continuously generated during normal equipment operation—vibration, temperature, pressure, current, gas flow, RF power, etc.—to establish equipment "health baselines." When real-time data deviates from the baseline beyond preset thresholds, the system issues early warnings and estimates Remaining Useful Life (RUL). This enables maintenance teams to perform preventive replacements during the next planned down period, avoiding unplanned outages.

Unique Challenges of Semiconductor Equipment PdM

Compared to traditional manufacturing, semiconductor equipment PdM faces several unique challenges. First, chamber clean cycle complexity: Thin-film deposition equipment like CVD and PVD requires periodic chamber cleans, after which equipment characteristics undergo step-change shifts that traditional trend analysis cannot handle. Moyne and Iskandar^[6] recommend a "segmented modeling" strategy—dividing the equipment lifecycle by clean events into multiple sub-cycles, modeling and predicting degradation trends independently within each sub-cycle.

Second, multi-component co-degradation: A single piece of equipment comprises thousands of components whose degradation rates influence each other. For example, RF generator power drift changes plasma state, which in turn accelerates chamber wall corrosion. Analyzing individual component degradation trends in isolation may miss these systemic effects. Graph neural networks (GNNs) and dynamic Bayesian networks are being explored for modeling degradation correlations between components^[1].

Third, integration with process quality: In semiconductor manufacturing, the ultimate goal of equipment maintenance is not just preventing breakdowns but ensuring process quality stability. Even if equipment hasn't failed, if its process output quality has begun to drift, maintenance intervention is still needed. This requires PdM systems to deeply integrate with FDC and VM systems—jointly analyzing equipment health status with process quality metrics to achieve "quality-aware maintenance." Chen's^[8] research validated this integrated strategy's effectiveness, improving the proportion of yield fluctuations attributable to equipment degradation by 35%.

7. AI-Driven Scheduling and Capacity Optimization

Fab production scheduling is one of the most complex problems in combinatorial optimization. A typical 12-inch fab simultaneously runs dozens of different products, each requiring hundreds of process steps with strict precedence and timing constraints (e.g., photoresist must be exposed within a specific time after coating), limited equipment interchangeability (certain process steps can only run on specific tools), and equipment status that constantly changes due to maintenance, breakdowns, and quality issues. These interwoven constraints form a dynamic, highly constrained scheduling problem whose solution space far exceeds traditional algorithm capabilities.

Moyne and Iskandar^[6] positioned production scheduling within their big data analytics framework as the core application of "prescriptive analytics" in semiconductor smart manufacturing. Traditional scheduling systems (such as dispatch rules) use simple priority rules (FIFO, shortest processing time first), which perform adequately in steady state but lack adaptability when facing equipment failures, rush orders, or material shortages. AI-driven scheduling systems can learn from historical scheduling decisions and outcomes, quickly generating near-optimal scheduling solutions when facing new disruptions.

Reinforcement Learning in Scheduling Applications

In recent years, deep reinforcement learning (DRL) has shown significant potential in semiconductor scheduling. DRL models scheduling as a sequential decision process—at each decision point (such as when a tool finishes its current job and is ready for the next lot), an AI agent selects the optimal dispatch decision based on current system state (all lot positions, equipment status, order priorities). Through extensive interaction training with fab simulators, DRL agents can learn complex scheduling policies that optimally balance multiple objectives (maximizing throughput, minimizing cycle time, meeting delivery commitments)^[7].

Beyond real-time scheduling, AI has important applications at the capacity planning level. Capacity planning decisions span weeks to months, involving equipment procurement decisions, maintenance schedule planning, headcount allocation, and new product introduction timing. Lee et al.'s^[7] Industrial AI framework emphasizes that capacity planning AI systems need to integrate demand forecasting, equipment reliability prediction, and supply chain information into an end-to-end decision support system. In Taiwan's foundry model, dynamic changes in customer mix (different customers with varying product mixes, priorities, and yield maturity) make capacity planning even more complex. AI's value lies in rapidly simulating capacity allocation scenarios under different customer mixes, helping business teams make optimal order acceptance decisions.

SEMI's report^[5] shows that global fab capital expenditure continues to climb, with corresponding equipment investment payback pressure. In this context, squeezing every bit of capacity from existing equipment through AI scheduling and capacity optimization—improving overall equipment effectiveness (OEE) by 3–5 percentage points—can have economic benefits equivalent to deferring construction of a new fab. This makes AI scheduling optimization one of the highest-ROI AI investment areas for semiconductor companies.

8. Taiwan's Semiconductor Industry AI Transformation Strategy

Taiwan's dominance in the global semiconductor supply chain is beyond doubt, but whether this position can be sustained depends on the industry's ability to continue leading at the frontier of technical efficiency. AI-driven smart manufacturing is not merely an efficiency tool but Taiwan's next competitive moat for semiconductors. However, the path to AI transformation has unique considerations and challenges within Taiwan's industry context.

Tiered Advancement Strategy

Based on Taiwan's semiconductor industry structure, we recommend a three-tier advancement strategy. Tier 1: Comprehensive deployment at industry leaders (TSMC, ASE, MediaTek). TSMC has established an AI team exceeding 1,000 people, deploying AI systems across wafer defect inspection, VM, yield prediction, and equipment PdM. Lee et al.'s^[7] Industrial AI vision—advancing from "data-driven" to "AI-driven" autonomous manufacturing—is gradually being realized at these leaders. ASE is also at the forefront with AI visual inspection and warpage prediction for advanced packaging. For these leaders, the AI challenge is not technology adoption but how to integrate AI applications scattered across business units into a unified smart manufacturing platform with continuous learning and evolution mechanisms.

Tier 2: Strategic adoption at mid-sized semiconductor companies (such as Powertech, ChipMOS, Win Semiconductors, GlobalWafers, etc.). These companies typically have annual revenues ranging from hundreds of millions to billions of NT dollars, with adequate IT infrastructure and engineering capabilities but relatively limited AI expertise and budgets. Kang and Cho's^[1] research provides a pragmatic adoption framework for such companies—start with a single high-value scenario, validate business value through a 3–6 month rapid PoC, then gradually expand. We recommend these companies prioritize: equipment PdM (reducing maintenance costs and unplanned downtime), AI visual quality inspection (replacing manual inspection, improving consistency), and VM (reducing metrology bottlenecks). These three scenarios have the highest technical maturity, clearest ROI, and mature reference solutions available.

Tier 3: AI enablement for equipment and materials suppliers (such as Gudeng, Home Beam, Topco, etc.). Semiconductor equipment and materials suppliers are the most easily overlooked but opportunity-rich segment in AI transformation. These companies can embed AI capabilities in their products—for example, equipment suppliers building PdM functionality into shipped equipment, materials suppliers using AI to optimize their process formulations—thereby increasing product added value and strengthening customer stickiness. Moyne and Iskandar^[6] observed that equipment intelligence is the foundation of the smart manufacturing ecosystem; improving AI capabilities at the equipment level drives intelligence upgrades across the entire industry chain.

Data Infrastructure and Talent Strategy

Regardless of tier, the foundation of AI transformation is data infrastructure. Chien et al.^[2] repeatedly emphasized in their research that semiconductor manufacturing data characteristics—high dimensionality, high sampling rate, multi-source heterogeneity, and strong temporal nature—impose stringent requirements on data architecture. We recommend Taiwanese semiconductor companies complete the following data infrastructure work before launching AI projects: unified data platform construction (bridging data silos across MES, FDC, SPC, EDA, and other systems), data quality governance mechanisms (defining data standards, cleansing processes, and quality metrics), and data security architecture design (ensuring sensitive process data confidentiality during AI model training and deployment).

Talent is the scarcest resource in AI transformation. Semiconductor AI requires cross-domain talent with combined expertise in semiconductor physics, process engineering, and machine learning—extremely scarce globally. Chen^[8] recommends companies adopt a "T-shaped talent" development strategy—having process engineers learn basic AI tools (Python and scikit-learn) while data scientists gain deep understanding of semiconductor physics—enabling collaboration on a common foundation. Taiwan's university semiconductor programs (such as NTU, NTHU, NYCU, and NCKU's electrical engineering and materials science departments) can add AI modules to their curricula to supply the next generation of cross-domain talent. Meanwhile, partnering with consulting teams possessing deep technical research capabilities can accelerate initial project delivery while building internal team capabilities.

9. Conclusion: Taiwan's Next Competitive Moat in Semiconductors

From wafer defect inspection to Virtual Metrology, from yield prediction to advanced packaging optimization, from equipment predictive maintenance to intelligent production scheduling—this article has systematically analyzed the current state, technical challenges, and development directions of AI applications across core semiconductor manufacturing functions. These are not isolated technology demonstrations but an interconnected smart manufacturing panorama: defect inspection results feed back into yield prediction models, VM predictions drive R2R process control, PdM maintenance schedules integrate into production scheduling systems—each AI application's value is amplified through integration with others.

Taiwan's semiconductor industry success over past decades was built on continuous process technology breakthroughs, manufacturing scale economics, and industry cluster synergies. However, as the global semiconductor landscape reshuffles—with the US, Europe, and Japan offering massive subsidies to attract semiconductor investments—Taiwan's traditional advantages face unprecedented challenges. Research from Moyne and Iskandar^[6] and Lee et al.^[7] converge on a core insight: in a semiconductor industry with continuously escalating capital intensity, manufacturing intelligence—not merely capacity scale—will be the key factor determining long-term competitiveness.

SEMI's global fab forecast report^[5] shows that over 80 new fabs will begin operations globally in the next five years. In this trillion-dollar global arms race, AI is not an optional "value-add" but the core infrastructure determining whether these astronomical investments translate into maximized returns. Taiwan's semiconductor companies—from global leaders like TSMC to niche segment hidden champions—all need to elevate AI from the level of "technology innovation experiments" to "core competitive strategy."

Kang and Cho^[1] remind us in their review's conclusion that machine learning success in semiconductor manufacturing ultimately depends on the triangular synergy of technology, data, and people. The most advanced algorithms cannot compensate for low-quality data, perfect data cannot compensate for models designed without domain knowledge, and technology and data readiness cannot compensate for organizational cultural resistance to AI. Taiwan's semiconductor industry possesses the world's deepest accumulation of process knowledge, the densest industry cluster effects, and the most dedicated engineering culture—these are the ideal conditions for AI transformation success.

For companies planning or advancing semiconductor AI transformation, Meta Intelligence's research team, with solid academic research capabilities and industry practical experience, provides full-cycle technical support from strategic planning and proof of concept to scaled deployment. We firmly believe that AI-driven smart manufacturing is not merely an efficiency improvement but the critical moat for Taiwan's semiconductor industry to maintain global leadership over the next decade. In this technology race that impacts national industrial destiny, every day of delay is an opportunity cost. Now is the best time to begin the journey.