fourier transform analysis

**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.