Abstract: Exemplary semiconductor processing systems may include a chamber body comprising sidewalls and a base. The systems may include a substrate support extending through the base of the chamber body. The substrate support may include a support plate defining a plurality of channels through an interior of the support plate. Each channel of the plurality of channels may include a radial portion extending outward from a central channel through the support plate. Each channel may also include a vertical portion formed at an exterior region of the support plate fluidly coupling the radial portion with a support surface of the support plate. The substrate support may include a shaft coupled with the support plate. The central channel may extend through the shaft. The systems may include a fluid source coupled with the central channel of the substrate support.

Abstract: Methods, apparatus, and processor-readable storage media for automated detection of unauthorized access to device screens using machine learning facial recognition techniques are provided herein. An example computer-implemented method includes automatically detecting, using one or more machine learning facial recognition techniques, at least a portion of at least one face within a given proximity of a user device; automatically performing a comparison, using one or more machine learning facial recognition techniques, of the detected portion of the at least one face with facial images attributed to a set of trusted individuals; and automatically performing security-related actions with respect to the user device upon determining, based at least in part on the comparison, that the detected portion of the at least one face exhibits at least a threshold level of distinctiveness relative to each of the facial images attributed to the set of trusted individuals.

Abstract: Methods, apparatus, and processor-readable storage media for automated detection of unauthorized access to device screens using machine learning facial recognition techniques are provided herein. An example computer-implemented method includes automatically detecting, using one or more machine learning facial recognition techniques, at least a portion of at least one face within a given proximity of a user device; automatically performing a comparison, using one or more machine learning facial recognition techniques, of the detected portion of the at least one face with facial images attributed to a set of trusted individuals; and automatically performing security-related actions with respect to the user device upon determining, based at least in part on the comparison, that the detected portion of the at least one face exhibits at least a threshold level of distinctiveness relative to each of the facial images attributed to the set of trusted individuals.