Predictive Control System for Precision Manufacturing
Built an advanced statistical model to control nano-scale positioning in semiconductor manufacturing, achieving sub-micron accuracy
The Challenge
Semiconductor manufacturing using roll-to-roll processes demands extraordinary precision, as positioning errors of even a few nanometers can ruin an entire production run. Traditional single-actuator control systems couldn't handle the complex interactions between multiple positioning actuators, leading to cumulative errors and quality issues.
I developed a multi-input predictive control system using Vectorial Auto-Regressive Moving Average (ARMAV) modeling to manage two coupled voice coil actuators for atomic force microscopy in microelectronics fabrication.
What I Built
Worked with high-frequency sensor data (1 MHz sampling rate, 48,000+ data points) from interferometer measurements to build a statistical model that could predict and correct positioning errors before they occurred.
Key Innovation
Created a coupled multi-input control strategy that treated the system holistically rather than as independent actuators—capturing dynamics that traditional approaches missed entirely.
Technical approach: Applied advanced signal processing (median filtering to preserve edge sharpness), statistical model selection (F-testing), and derived an optimal control law that minimizes both positioning error and energy consumption.
Impact & Results
95% Accuracy
Predictions within confidence intervals
0.044 MSE
Ultra-low positioning error achieved
48K+ Points
High-frequency data processed
Successfully validated the model through frequency-domain analysis, correctly identifying the system's 20 Hz natural frequency and drift characteristics. The ARMAV framework outperformed independent control models by capturing coupled actuator dynamics that would otherwise cause compounding errors in production.
Real-world readiness: Designed for implementation in actual manufacturing environments with adaptive control capabilities for production settings.
Full Technical Report
For detailed methodology, mathematical derivations, and comprehensive analysis: