Publications

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Accurate understanding of the behavior of commercial-off-the-shelf electrical devices is important in many applications. This paper discusses methods for the principled statistical analysis of electrical device data. We present several recent successful efforts and describe two current areas of research that we anticipate will produce widely applicable methods. Because much electrical device data is naturally treated as functional, and because such data introduces some complications in analysis, we focus on methods for functional data analysis.

Accurate characterization of electrical device behavior is a key component of developing accurate electrical models and assessing reliability. Measurements characterizing an electrical device can be produced from current-voltage (I-V) sweeps. We introduce the pairwise midpoint method (PMM) for estimating the mean of a functional data set and apply it to I-V sweeps from a Zener diode. Comparisons indicate that the PMM is a viable method for describing the mean behavior of a functional data set.

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Ultra-wide-bandgap aluminum gallium nitride (AlGaN) possesses several material properties that make it attractive for use in a variety of applications. This chapter focuses on power switching and radio-frequency (RF) devices based on Al-rich AlGaN heterostructures. The relevant figures of merit for both power switching and RF devices are discussed as motivation for the use of AlGaN heterostructures in such applications. The key physical parameters impacting these figures of merit include critical electric field, channel mobility, channel carrier density, and carrier saturation velocity, and the factors influencing these and the trade-offs between them are discussed. Surveys of both power switching and RF devices are given and their performance is described including in special operating regimes such as at high temperatures. Challenges to be overcome, such as the formation of low-resistivity Ohmic contacts, are presented. Finally, an overview of processing-related challenges, especially related to surfaces and interfaces, concludes the chapter.

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We present a Physics-Informed Graph Neural Network (pigNN) methodology for rapid and automated compact model development. It brings together the inherent strengths of data-driven machine learning, high-fidelity physics in TCAD simulations, and knowledge contained in existing compact models. In this work, we focus on developing a neural network (NN) based compact model for a non-ideal PN diode that represents one nonlinear edge in a pigNN graph. This model accurately captures the smooth transition between the exponential and quasi-linear response regions. By learning voltage dependent non-ideality factor using NN and employing an inverse response function in the NN loss function, the model also accurately captures the voltage dependent recombination effect. This NN compact model serves as basis model for a PN diode that can be a single device or represent an isolated diode in a complex device determined by topological data analysis (TDA) methods. The pigNN methodology is also applicable to derive reduced order models in other engineering areas.

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Measurements performed on a population of electronic devices reveal part-to-part variation due to manufacturing process variation. Corner models are a useful tool for the designers to bound the effect of this variation on circuit performance. To accurately simulate the circuit level behavior, compact model parameters for devices within a circuit must be calibrated to experimental data. However, determination of the bounding data for corner model calibration is difficult, primarily because available tolerance bound calculation methods only consider variability along one dimension and, do not adequately consider the variabilities across both the current and voltage axes. This paper presents the demonstration of a novel functional data analysis approach to generate tolerance bounds on these two types of variability separately and these bounds are then transformed to be used in corner model calibration.

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