Project Overview
Precision Manufacturing: Harnessing Machine Learning for Pass-Fail Yield Prediction
🔬👨🔬 A complex modern semiconductor manufacturing process is normally under constant surveillance via the monitoring of signals/variables collected from sensors and or process measurement points. 📊
However, not all of these signals are equally valuable in a specific monitoring system. The measured signals contain a combination of useful information, irrelevant information as well as noise. 📉📝❌
Engineers typically have a much larger number of signals than are actually required. If we consider each type of signal as a feature, then feature selection may be applied to identify the most relevant signals. 🛠️📈
The Process Engineers may then use these signals to determine key factors contributing to yield excursions downstream in the process. This will enable an increase in process throughput, decreased time to learning, and reduce per-unit production costs. 💹⏳💲
These signals can be used as features to predict the yield type. And by analyzing and trying out different combinations of features, essential signals that are impacting the yield type can be identified. 📊🔄💡
Data preprocessing:
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✨ Handle missing values
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📊 Encoding the Output Data
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🧐 Checking if the dataset contains any NULL values
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📊🧐 Taking care of outliers
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📏 Feature Scaling
Data Visualization:
- 📝🔍 Reviewing the data for some general information
Model Training:
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🧩 Split data into training and testing sets
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🚀 Train selected models on training data
ML Models:
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📊 Logistic regression
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🧠 ANN
Model Evaluation:
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📊 Evaluate model performance using accuracy metrics
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📉 Analyze confusion matrix
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📈 Plotting the ANN Learning Curve
Tuning:
- ⚙️ Hyperparameter tuning for model optimization
Dataset: SemiconductorManufacturingProcessDataset.csv