For wafer manufacturers like one of India's largest fabs, even small defect rates translate to massive costs. Undetected defects cause downstream failures, warranty claims, and reputation damage, while false alarms waste inspection resources and slow production. Yet defects are rare, buried in sensor data from systems generating 1,500+ sensor readings.

Binary sparsity analysis showing sensor activation patterns across 1,500+ features

I developed a custom anomaly detection system built from first principles, integrating deep EDA and statistical research, spike-pattern and sensor analysis, and multi-layer rule logic designed to capture the true physical behavior of the machinery.

Normal and anomalous firing patterns comparison

The resulting model outperforms standard ML baselines – including Random Forest, Logistic Regression, KNN, Hybrid Naïve Bayes, Neural Networks, XGBoost, and LightGBM – in interpretability, stability, and efficiency. This end-to-end approach demonstrates how interpretable models can exceed black-box performance in real industrial environments while delivering over 70% cost reduction through a business-first, insight-driven modeling framework.

Interested in a similar approach?
quique@databirds.ai