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The Future of Semiconductor Manufacturing Evolving with AI
Analysis Solutions That Maximize the Value of Test Data
In semiconductor manufacturing, wafer test and final test processes generate enormous, high-dimensional test data every day. These data are valuable sources of information that indicate process variation, device characteristics, tester behavior, and even the condition of equipment and fixtures—in other words, a treasure trove.
We apply methods such as statistical quality control (SQC), multivariate analysis (PCA, LDA, t-SNE, UMAP), clustering (k-means, DBSCAN), anomaly detection (Isolation Forest, autoencoder), and time-series prediction (ARIMA, LSTM) to large, noisy data sets. This enables highly accurate root cause analysis, trend identification, and failure prediction.
AI Analysis Platform Rooted in the Manufacturing Site
Our analysis environment is built on Python. We use pandas and NumPy for preprocessing, scikit-learn, TensorFlow, and XGBoost for model building, and matplotlib, Plotly, and Seaborn for visualization, presenting results both intuitively and quantitatively.
We also adopt MySQL-based data management that considers hierarchical structures such as Lot, Wafer, and Site. Through a sharded configuration, we achieve high-speed bulk loading and smooth access using optimized queries.
Delivering Value Directly Linked to Manufacturing KPIs
This analysis platform and expertise directly contribute to manufacturing KPIs such as reducing test costs, improving yield, and shortening TAT (Turn Around Time).
We provide solutions for realizing smart fabs that combine on-site perspectives with algorithmic approaches, helping shape the future of semiconductor manufacturing together with our customers.
Facing challenges with semiconductor test data?
Let's explore how your data can be analyzed effectively
We suggest analysis approaches tailored to your challenges,
including yield improvement, anomaly detection, and test cost optimization.