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This paper presents a predictive model for the negative bias temperature instability (NBTI) of PMOS under both short term and long term operation. Based on the reaction-diffusion (R-D) mechanism, this model accurately captures the dependence of NBTI on the oxide thickness (t<sub>ox</sub>), the diffusing species (H or H<sub>2</sub>) and other key transistor(More)
Negative bias temperature instability (NBTI) has become the dominant reliability concern for nanoscale PMOS transistors. In this paper, a predictive model is developed for the degradation of NBTI in both static and dynamic operations. Model scalability and generality are comprehensively verified with experimental data over a wide range of process and bias(More)
We describe the isolation and characterization of Drosophila synaptojanin (synj) mutants. synj encodes a phosphatidylinositol phosphatase involved in clathrin-mediated endocytosis. We show that Synj is specifically localized to presynaptic terminals and is associated with synaptic vesicles. The electrophysiological and ultrastructural defects observed in(More)
Within-die spatial correlation of device parameter values caused by manufacturing variations [1] has a significant impact on circuit performance. Based on experimental and simulation results, we (1) characterize the spatial correlation of gate length over a full-field range of horizontal and vertical separation; (2) develop a rudimentary spatial correlation(More)
Negative Bias Temperature Instability (NBTI) is the leading factor of circuit performance degradation. Due to its complex dependence on operating conditions, especially signal probability, it is a tremendous challenge to accurately predict the degradation rate in reality. On the other hand, we demonstrate in this work that it is feasible to reliably predict(More)
Convolutional Neural Networks (CNNs) have gained popularity in many computer vision applications such as image classification, face detection, and video analysis, because of their ability to train and classify with high accuracy. Due to multiple convolution and fully-connected layers that are compute-/memory-intensive, it is difficult to perform real-time(More)