Optical Emission Spectrum Processing Using Wavelet Compression During Wafer Fabrication

This paper describes the application of discrete-wavelet transform (DWT)-based algorithms to compress optical emission spectral intensity data collected during wafer fabrication. The major goal is to seek computationally efficient compression algorithms that can significantly reduce the data storage requirement but also are capable of retaining enough data authenticity critical for diagnostic purposes. The potential benefits will provide a promising foundation for an integrated data collection and compression tool. The optical emission data are treated in this paper either as images for the whole spectrum or bands of time series and are treated accordingly using appropriate DWT compression approaches. We have found through representative simulation examples that using the set partitioning in hierarchical trees compression algorithm it is achievable to obtain better than 99% storage reduction for optical emission spectrum (OES) images. For OES time series we have achieved around 95% storage reduction with Daubechies and Haar wavelets. The storage reductions are achieved while maintaining sufficient authenticity to retain the dynamic nature of OES data for diagnostic purposes.