Abstract:
Methods and apparatus for semiconductor manufacturing process monitoring and control are provided herein. In some embodiments, apparatus for substrate processing may include a process chamber for processing a substrate in an inner volume of the process chamber; a radiation source disposed outside of the process chamber to provide radiation at a frequency of about 200 GHz to about 2 THz into the inner volume via a dielectric window in a wall of the vacuum process chamber; a detector to detect the signal after having passed through the inner volume; and a controller coupled to the detector and configured to determine the composition of species within the inner volume based upon the detected signal.
Abstract:
Methods for etching silicon-based antireflective layers are provided herein. In some embodiments, a method of etching a silicon-based antireflective layer may include providing to a process chamber a substrate having a multiple-layer resist thereon, the multiple-layer resist comprising a patterned photoresist layer defining features to be etched into the substrate disposed above a silicon-based antireflective coating; and etching the silicon-based antireflective layer through the patterned photoresist layer using a plasma formed from a process gas having a primary reactive agent comprising a chlorine-containing gas. In some embodiments, the chlorine-containing gas is chlorine (Cl 2 ).
Abstract:
Implementations disclosed describe a method and a system to perform the method of obtaining a reduced representation of a plurality of sensor statistics representative of data collected by a plurality of sensors associated with a device manufacturing system performing a manufacturing operation. The method further includes generating, using a plurality of outlier detection models, a plurality of outlier scores, each of the plurality of outlier scores generated based on the reduced representation of the plurality of sensor statistics using a respective one of the plurality of outlier detection models. The method further includes processing the plurality of outlier scores using a detector neural network to generate an anomaly score indicative of a likelihood of an anomaly associated with the manufacturing operation.