Time Series Analysis

Time Series Analysis in semiconductor manufacturing is the study of sequential process data ordered by time — analyzing trends, seasonality, autocorrelation, and change points to understand process dynamics, predict future behavior, and detect shifts.

Key Time Series Methods

• Trend Analysis: Moving averages, exponential smoothing, and regression for identifying long-term drift.
• ARIMA: Auto-Regressive Integrated Moving Average models for forecasting and anomaly detection.
• Change Point Detection: CUSUM, PELT algorithms detect when the process mean or variance shifts.
• Spectral Analysis: FFT reveals periodic patterns (shift effects, PM cycles, seasonal variations).

Why It Matters

• Drift Detection: Identifies gradual process drift before it exceeds specification limits.
• PM Scheduling: Correlates time patterns with preventive maintenance cycles.
• Forecasting: Predicts future process state to enable proactive corrections.

Time Series Analysis is reading the process heartbeat — understanding how process parameters evolve over time to detect, predict, and prevent excursions.