Transient electrothermal simulation of power semiconductor devices

In this paper, a new thermal model based on the Fourier series solution of heat conduction equation has been introduced in detail. 1-D and 2-D Fourier series thermal models have been programmed in MATLAB/Simulink. Compared with the traditional finite-difference thermal model and equivalent RC thermal network, the new thermal model can provide high simulation speed with high accuracy, which has been proved to be more favorable in dynamic thermal characterization on power semiconductor switches. The complete electrothermal simulation models of insulated gate bipolar transistor (IGBT) and power diodes under inductive load switching condition have been successfully implemented in MATLAB/Simulink. The experimental results on IGBT and power diodes with clamped inductive load switching tests have verified the new electrothermal simulation model. The advantage of Fourier series thermal model over widely used equivalent RC thermal network in dynamic thermal characterization has also been validated by the measured junction temperature

Principles of Neuromorphic Photonics

In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure

Physics-based large-signal sensitivity analysis of microwave circuits using technological parametric sensitivity from multidimensional semiconductor device models

The authors present an efficient approach to evaluate the large-signal (LS) parametric sensitivity of active semiconductor devices under quasi-periodic operation through accurate, multidimensional physics-based models. The proposed technique exploits efficient intermediate mathematical models to perform the link between physics-based analysis and circuit-oriented simulations, and only requires the evaluation of dc and ac small-signal (dc charge) sensitivities under general quasi-static conditions. To illustrate the technique, the authors discuss examples of sensitivity evaluation, statistical analysis, and doping profile optimization of an implanted MESFET to minimize intermodulation which makes use of LS parametric sensitivities under two-tone excitatio

Solcore: A multi-scale, python-based library for modelling solar cells and semiconductor materials

Computational models can provide significant insight into the operation mechanisms and deficiencies of photovoltaic solar cells. Solcore is a modular set of computational tools, written in Python 3, for the design and simulation of photovoltaic solar cells. Calculations can be performed on ideal, thermodynamic limiting behaviour, through to fitting experimentally accessible parameters such as dark and light IV curves and luminescence. Uniquely, it combines a complete semiconductor solver capable of modelling the optical and electrical properties of a wide range of solar cells, from quantum well devices to multi-junction solar cells. The model is a multi-scale simulation accounting for nanoscale phenomena such as the quantum confinement effects of semiconductor nanostructures, to micron level propagation of light through to the overall performance of solar arrays, including the modelling of the spectral irradiance based on atmospheric conditions. In this article we summarize the capabilities in addition to providing the physical insight and mathematical formulation behind the software with the purpose of serving as both a research and teaching tool.Comment: 25 pages, 18 figures, Journal of Computational Electronics (2018

Complementary Symmetry Nanowire Logic Circuits: Experimental Demonstrations and in Silico Optimizations

Complementary symmetry (CS) Boolean logic utilizes both p- and n-type field-effect transistors (FETs) so that an input logic voltage signal will turn one or more p- or n-type FETs on, while turning an equal number of n- or p-type FETs off. The voltage powering the circuit is prevented from having a direct pathway to ground, making the circuit energy efficient. CS circuits are thus attractive for nanowire logic, although they are challenging to implement. CS logic requires a relatively large number of FETs per logic gate, the output logic levels must be fully restored to the input logic voltage level, and the logic gates must exhibit high gain and robust noise margins. We report on CS logic circuits constructed from arrays of 16 nm wide silicon nanowires. Gates up to a complexity of an XOR gate (6 p-FETs and 6 n-FETs) containing multiple nanowires per transistor exhibit signal restoration and can drive other logic gates, implying that large scale logic can be implemented using nanowires. In silico modeling of CS inverters, using experimentally derived look-up tables of individual FET properties, is utilized to provide feedback for optimizing the device fabrication process. Based upon this feedback, CS inverters with a gain approaching 50 and robust noise margins are demonstrated. Single nanowire-based logic gates are also demonstrated, but are found to exhibit significant device-to-device fluctuations

A theoretical study of heterojunction and graded band gap type solar cells

A computer program was designed for the analysis of variable composition solar cells and applied to several proposed solar cell structures using appropriate semiconductor materials. The program simulates solar cells made of a ternary alloy of two binary semiconductors with an arbitrary composition profile, and an abrupt or Gaussian doping profile of polarity n-on-p or p-on-n with arbitrary doping levels. Once the device structure is specified, the program numerically solves a complete set of differential equations and calculates electrostatic potential, quasi-Fermi levels, carrier concentrations and current densities, total current density and efficiency as functions of terminal voltage and position within the cell. These results are then recorded by computer in tabulated or plotted form for interpretation by the user