Fab and defect inspection workflows have reached an inflection point with the introduction of (GAA) and high aspect ratio memory structures. Few existing inspection and metrology tools can match the sub-surface imaging and analytical capability provided by (Scanning) Transmission Electron Microscopy (). Fully automated (S)TEM workflows are becoming a necessity for the industry to deliver high volume metrology reference data. This atomic scale data must be available with fast turnaround and must also be statistically valid to speed up learning cycles. In this study, we present (S)TEM metrology characterization of advanced GAA and devices by an automated MetriosTM TEM. We introduce an internal -based modeling algorithm to address the challenges of recognizing GAA devices with process variations and provide faster access to highly accurate TEM reference metrology data. We present automated characterization of beam-sensitive ONO layers, which is a key challenge in 3D NAND device metrology, enabled by a new generation of EDS detector with a high collection efficiency. We also present results on (S)TEM metrology during process monitoring of GAA devices with a higher level of TEM .

Abstract:The moments prior to the start of class provide many opportunities for engaging with students in a less formal setting. These moments can be used to establish a pre-class environment conducive to student motivation, focus, confidence, and ultimately the achievement of learning objectives. For example, the pre-class environment could include informal conversations between the students and instructor. These conversations can help the students develop rapport with the instructor, while giving the instructor an opportunity to evaluate student perceptions about course difficulty or workload. The pre-class environment could also be non-conversational. For example, playing music that is topically related to the class subject (e.g. playing "Good Vibrations" before delivering a lecture on mechanical vibrations) may increase motivation, focus attention, or generate excitement. As instructors will often have a preferred pre-class environment, there is a large amount of anecdotal evidence regarding its effect on student attitudes. However, there is little published material discussing on the impact (if any) of the pre-class environment on student learning objectives. This study uses a multi-dimensional experimental model to measure the impact of the pre-class environment on both student attitudes and learning objectives. Three different pre-class environments are tested: 1) informal conversations, 2) topical music, and 3) no activity. The influence of other variables such as gender, year, and major are also examined. Recommendations to enhance both are given based on the findings. Given the potential benefit, this work also examines some of the practical aspects of pre-class activities, including instructor preferences and the transition to regular class time.Background:

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A book review of A Pocket Guide to Online Teaching: Translating the Evidence-Based Model Teaching Criteria, by Aaron Richmond, Regan Gurung, and Guy Boysen.