Advanced Scanning Electron Microscopy Methods and Applications to Integrated Circuit Failure Analysis

Semiconductor device failure analysis using the scanning electron microscope (SEM) has become a standard component of integrated circuit fabrication. Improvements in SEM capabilities and in digital imaging and processing have advanced standard acquisition modes and have promoted new failure analysis methods. The physical basis of various data acquisition modes, both standard and new, and their implementation on a computer controlled SEM image acquisition/processing system are discussed, emphasizing the advantages of each method. Design considerations for an integrated, online failure analysis system are also described. Recent developments in the integration of the information provided by electron beam analysis, conventional integrated circuit (IC) testing, computer-aided design (CAD), and device parameter testing into a single system promise to provide powerful future tools for failure analysis

Data Acquisition and Processing Techniques for Voltage Contrast Measurements

The effects of several data acquisition techniques on the accuracy of voltage contrast measurements are studied. In particular, the effect of using a voltage reference region directly connected to an external voltage source in performing the image intensity-to-voltage mapping of a node whose voltage is to be determined is examined. This is found to allow improved voltage measurement. The actual reference curves were obtained by least squares fitting the measured intensity-voltage reference data alternately to a quadratic and a cubic function. In addition, various mapping algorithms are considered including ones based alternately on the use of unprocessed, subtracted and normalized data. Using these techniques, one should expect voltage errors with means of approximately 25 mV and standard deviations of approximately 160 mV even with an unmodified commercial SEM incorporating no additional hardware to increase precision

Optimal control as a graphical model inference problem

We reformulate a class of non-linear stochastic optimal control problems introduced by Todorov (2007) as a Kullback-Leibler (KL) minimization problem. As a result, the optimal control computation reduces to an inference computation and approximate inference methods can be applied to efficiently compute approximate optimal controls. We show how this KL control theory contains the path integral control method as a special case. We provide an example of a block stacking task and a multi-agent cooperative game where we demonstrate how approximate inference can be successfully applied to instances that are too complex for exact computation. We discuss the relation of the KL control approach to other inference approaches to control.Comment: 26 pages, 12 Figures; Machine Learning Journal (2012