Abstract: The present disclosure relates to energy consumption of tools and, more particularly, to modeling energy consumption in semiconductor tools. The method includes: obtain, by a computing device, a state history of a tool over a predetermined period of time; model, by the computing device, the state history of the tool using machine learning to provide a predicted energy usage of the tool; compare, by the computing device, predicted energy usage obtained from the machine learning model to an actual energy usage of the tool; and determine, by the computing device, a deviation between the actual energy usage and the predicted energy usage of the tool to maintain energy consumption efficiency of the tool.

Abstract: A method for providing a compressed bit fail map, in accordance with the invention includes the steps of testing a semiconductor device to determine failed devices and transferring failure information to display a compressed bit map by designating areas of the bit map for corresponding failure locations on the semiconductor device. Failure classification is provided by designating shapes and dimensions of fail areas in the designated areas of the bit map such that the fail area shapes and dimensions indicate a fail type.