Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

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.

Abstract not provided.

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.

Abstract not provided.

Abstract not provided.

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.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Nominal behavior selection of an electronic device from a measured dataset is often difficult. Device characteristics are rarely monotonic and choosing the single device measurement which best represents the center of a distribution across all regions of operation is neither obvious nor easy to interpret. Often, a device modeler uses a degree of subjectivity when selecting nominal device behavior from a dataset of measurements on a group of devices. This paper proposes applying a functional data approach to estimate the mean and nominal device of an experimental dataset. This approach was applied to a dataset of electrical measurements on a set of commercially available Zener diodes and proved to more accurately represent the average device characteristics than a point-wise calculation of the mean. It also enabled an objective method for selecting a nominal device from a dataset of device measurements taken across the full operating region of the Zener diode.

Abstract not provided.

Abstract not provided.

This report presents a detailed process for compact model parameter extraction for DC circuit Zener diodes. Following the traditional approach of Zener diode parameter extraction, circuit model representation is defined and then used to capture the different operational regions of a real diode's electrical behavior. The circuit model contains 9 parameters represented by resistors and characteristic diodes as circuit model elements. The process of initial parameter extraction, the identification of parameter values for the circuit model elements, is presented in a way that isolates the dependencies between certain electrical parameters and highlights both the empirical nature of the extraction and portions of the real diode physical behavior which of the parameters are intended to represent. Optimization of the parameters, a necessary part of a robost parameter extraction process, is demonstrated using a 'Xyce-Dakota' workflow, discussed in more detail in the report. Among other realizations during this systematic approach of electrical model parameter extraction, non-physical solutions are possible and can be difficult to avoid because of the interdependencies between the different parameters. The process steps described are fairly general and can be leveraged for other types of semiconductor device model extractions. Also included in the report are recommendations for experiment setups for generating optimum dataset for model extraction and the Parameter Identification and Ranking Table (PIRT) for Zener diodes.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Due to the ultra-wide bandgap of Al-rich AlGaN, up to 5.8 eV for the structures in this study, obtaining low resistance ohmic contacts is inherently difficult to achieve. A comparative study of three different fabrication schemes is presented for obtaining ohmic contacts to an Al-rich AlGaN channel. Schottky-like behavior was observed for several different planar metallization stacks (and anneal temperatures), in addition to a dry-etch recess metallization contact scheme on Al0.85Ga0.15N/Al0.66Ga0.34N. However, a dry etch recess followed by n+-GaN regrowth fabrication process is reported as a means to obtain lower contact resistivity ohmic contacts on a Al0.85Ga0.15N/Al0.66Ga0.34N heterostructure. Specific contact resistivity of 5 × 10−3 Ω cm2 was achieved after annealing Ti/Al/Ni/Au metallization.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.