Abstract: A method of predicting virtual metrology data for a wafer lot that includes receiving first image data from an imager system, the first image data relating to at least one first wafer lot, receiving measured metrology data from metrology equipment relating to the at least one first wafer lot, applying one or more machine learning techniques to the first image data and the measured metrology data to generate at least one predictive model for predicting at least one of virtual metrology data or virtual cell metrics data of wafer lots, and utilizing the at least one generated predictive model to generate at least one of first virtual metrology data or first virtual cell metrics data for the first wafer lot.

Abstract: Static data that represents one or more static contributing factors to a dynamic threat-level index is received for a corresponding local region and live data that represents one or more dynamic contributing factors to the dynamic threat-level index for the corresponding local region is repeatedly received. An updated dynamic threat-level index for the corresponding local region is repeatedly determined based at least in part on a previously determined updated dynamic threat-level index for the corresponding local region, at least some of the received static data for the corresponding local region, and at least some of the received live data for the corresponding local region. A dashboard is displayed that dynamically shows the updated dynamic threat-level index for each of at least some of the plurality of local regions.

Abstract: A method of predicting virtual metrology data for a wafer lot that includes receiving first image data from an imager system, the first image data relating to at least one first wafer lot, receiving measured metrology data from metrology equipment relating to the at least one first wafer lot, applying one or more machine learning techniques to the first image data and the measured metrology data to generate at least one predictive model for predicting at least one of virtual metrology data or virtual cell metrics data of wafer lots, and utilizing the at least one generated predictive model to generate at least one of first virtual metrology data or first virtual cell metrics data for the first wafer lot.

Abstract: A method of predicting virtual metrology data for a wafer lot that includes receiving first image data from an imager system, the first image data relating to at least one first wafer lot, receiving measured metrology data from metrology equipment relating to the at least one first wafer lot, applying one or more machine learning techniques to the first image data and the measured metrology data to generate at least one predictive model for predicting at least one of virtual metrology data or virtual cell metrics data of wafer lots, and utilizing the at least one generated predictive model to generate at least one of first virtual metrology data or first virtual cell metrics data for the first wafer lot.

Abstract: A method of predicting virtual metrology data for a wafer lot that includes receiving first image data from an imager system, the first image data relating to at least one first wafer lot, receiving measured metrology data from metrology equipment relating to the at least one first wafer lot, applying one or more machine learning techniques to the first image data and the measured metrology data to generate at least one predictive model for predicting at least one of virtual metrology data or virtual cell metrics data of wafer lots, and utilizing the at least one generated predictive model to generate at least one of first virtual metrology data or first virtual cell metrics data for the first wafer lot.

Abstract: A method of predicting virtual metrology data for a wafer lot that includes receiving first image data from an imager system, the first image data relating to at least one first wafer lot, receiving measured metrology data from metrology equipment relating to the at least one first wafer lot, applying one or more machine learning techniques to the first image data and the measured metrology data to generate at least one predictive model for predicting at least one of virtual metrology data or virtual cell metrics data of wafer lots, and utilizing the at least one generated predictive model to generate at least one of first virtual metrology data or first virtual cell metrics data for the first wafer lot.

Abstract: Apparatus, systems, and methods to manage memory operations are described. In one example, a controller comprises logic to receive a first transaction to operate on a first data element in the cache memory, perform a lookup operation for the first data element in the volatile memory and in response to a failed lookup operation, to generate a cache scrub hint forward the cache scrub hint to a cache scrub engine and identify one or more cache lines to scrub based at least in part on the cache scrub hint. Other examples are also disclosed and claimed.

Abstract: Apparatus, systems, and methods to manage memory operations are described. In one example, a controller comprises logic to receive a first transaction to operate on a first data element in the cache memory, perform a lookup operation for the first data element in the volatile memory and in response to a failed lookup operation, to generate a cache scrub hint forward the cache scrub hint to a cache scrub engine and identify one or more cache lines to scrub based at least in part on the cache scrub hint. Other examples are also disclosed and claimed.

Abstract: A processor includes an execution unit, a memory subsystem, and a memory management unit (MMU). The MMU includes logic to evaluate a first bandwidth usage of the memory subsystem and logic to evaluate a second bandwidth usage between the processor and a memory. The memory is communicatively coupled to the memory subsystem. The memory subsystem is to implement a cache for the memory. The MMU further includes logic to evaluate a request of the memory subsystem, and, based upon the first bandwidth usage and the second bandwidth usage, fulfill the request by bypassing the memory subsystem.

Abstract: Apparatus and methods for detecting molecules in a fluidic environment are provided, including nanodevices and methods for fabricating, functionalizing, and operating such nanodevices. At least one of the methods includes selective heating of nanodevices in an array.