Fourier Transform Analysis in semiconductor data is the decomposition of time-domain or spatial-domain signals into their frequency components — revealing periodic patterns, resonances, and cyclic variations that are hidden in the raw time/space domain data.

Applications in Semiconductor Manufacturing

• Vibration Analysis: FFT of accelerometer data identifies equipment resonance frequencies.
• Process Periodicity: Reveals PM-cycle effects, shift patterns, and seasonal variation.
• Wafer Map Analysis: 2D FFT of wafer maps identifies periodic spatial patterns (spinner marks, slit effects).
• Spectral Filtering: Remove noise at specific frequencies while preserving the signal of interest.

Why It Matters

• Hidden Periodicity: Periodic disturbances (rotation speed, scan frequency) are obvious in frequency domain but invisible in time domain.
• Root Cause: Frequency peaks directly correspond to physical mechanisms (motor RPM, scan rate, gas pulsing).
• Signal Processing: FFT-based filtering removes noise while preserving the underlying trend.

Fourier Transform Analysis is finding the rhythm in fab data — converting time-domain signals to frequency domain to reveal hidden periodic patterns.