Abstract: An embodiment for speech-to-text auto-scaling of computational resources is provided. The embodiment may include computing a delta for each word in a transcript between a wall clock time and a time when the word is delivered to a client. The embodiment may also include submitting the deltas to a group of metrics servers. The embodiment may further include requesting from the group of metrics servers current values of the deltas. The embodiment may also include determining whether the current values of the deltas exceed a pre-defined max-latency threshold. The embodiment may further include adjusting the allocated computational resources based on a frequency of the current values of the deltas that exceed the pre-defined max-latency threshold. The embodiment may also include creating a histogram from the current values of the deltas and scaling-up the allocated computational resources based on a percentage of data points that fall above the pre-defined max-latency threshold.

Abstract: An embodiment for speech-to-text auto-scaling of computational resources is provided. The embodiment may include computing a delta for each word in a transcript between a wall clock time and a time when the word is delivered to a client. The embodiment may also include submitting the deltas to a group of metrics servers. The embodiment may further include requesting from the group of metrics servers current values of the deltas. The embodiment may also include determining whether the current values of the deltas exceed a pre-defined max-latency threshold. The embodiment may further include adjusting the allocated computational resources based on a frequency of the current values of the deltas that exceed the pre-defined max-latency threshold. The embodiment may also include creating a histogram from the current values of the deltas and scaling-up the allocated computational resources based on a percentage of data points that fall above the pre-defined max-latency threshold.

Abstract: A method of monitoring the status of plasma in a chamber using real-time spectral data while conducting an etch process during the course of manufacturing of semiconductor wafers. Spectral data is collected during etching, with the spectral data characterizing an emission of light from etch species contained in the plasma, and maintaining the collected data as reference data. A model of principal components of the data is generated. Additional spectral data is extracted from the plasma and compared with the model. Discrepancies pinpoint the presence of foreign material faults and help determine the cause of the failures to ensure appropriate corrective action.