scholarly journals Comparison of the gate drive parameter space for driving power MOSFETs using conventional and cascode configurations

2014 ◽  
Author(s):  
Mark A. H. Broadmeadow ◽  
Geoffrey R. Walker ◽  
Gerard F. Ledwich
Keyword(s):  
Parameter Space ◽  
Power Mosfets ◽  
Drive Parameter

Matlab/Simulink Modeling of SiC Power MOSFETs

2014 ◽  
Vol 9 (4) ◽  
pp. 671 ◽  
Author(s):  
Paolo Giammatteo ◽  
Concettina Buccella ◽  
Carlo Cecati
Keyword(s):  
Matlab Simulink ◽  
Power Mosfets

Integrated ESD Robustness through Device Analysis of Ultra-Small Low Voltage Power MOSFETs

2014 ◽  
Author(s):  
Ian Kearney ◽  
Hank Sung
Keyword(s):  
New Product Development ◽  
Silicon Oxide ◽  
Low Voltage ◽  
Photon Emission ◽  
Electrical Characterization ◽  
Power Mosfets ◽  
Low Leakage ◽  
A Cell ◽  
Thermographic Analysis ◽  
Voltage Spike

Abstract Low voltage power MOSFETs often integrate voltage spike protection and gate oxide ESD protection. The basic concept of complete-static protection for the power MOSFETs is the prevention of static build-up where possible and the quick, reliable removal of existing charges. The power MOSFET gate is equivalent to a low voltage low leakage capacitor. The capacitor plates are formed primarily by the silicon gate and source metallization. The capacitor dielectric is the silicon oxide gate insulation. Smaller devices have less capacitance and require less charge per volt and are therefore more susceptible to ESD than larger MOSFETs. A FemtoFETTM is an ultra-small, low on-resistance MOSFET transistor for space-constrained handheld applications, such as smartphones and tablets. An ESD event, for example, between a fingertip and the communication-port connectors of a cell phone or tablet may cause permanent system damage. Through electrical characterization and global isolation by active photon emission, the authors identify and distinguish ESD failures. Thermographic analysis provided additional insight enabling further separation of ESD failmodes. This paper emphasizes the role of failure analysis in new product development from the create phase through to product ramp. Coupled with device electrical simulation, the analysis observations led to further design enhancement.

Investigation of the applicability domains of the bubble cluster mathematical model

2011 ◽  
Vol 8 (1) ◽  
pp. 65-73
Author(s):  
E.Sh. Nasibullaeva ◽  
I.Sh. Akhatov
Keyword(s):  
Mathematical Model ◽  
Parameter Space ◽  
Acoustic Field ◽  
Initial Radius ◽  
Large Drop ◽  
Bubble Cluster ◽  
The Mathematical Model

The mathematical model of a bubble cluster subjected to an acoustic field is investigated. In this model the cluster is considered as a large drop containing a liquid and a set of microbubbles. Areas of applicability of the mathematical model of the bubble cluster in the parameter space (α, R_0) are constructed, where α is the bubble concentration in the cluster; R_0 is the initial radius of the cluster.

Rapid Characterization and Parameter Space Exploration of Perovskites Using an Automated Routine

2019 ◽  
Vol 22 (1) ◽  
pp. 6-17 ◽  
Author(s):  
Elisabeth Reinhardt ◽  
Ahmed M. Salaheldin ◽  
Monica Distaso ◽  
Doris Segets ◽  
Wolfgang Peukert
Keyword(s):  
Parameter Space ◽  
Space Exploration ◽  
Rapid Characterization

scholarly journals Search Acceleration of Evolutionary Multi-Objective Optimization Using an Estimated Convergence Point

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 129 ◽  
Author(s):  
Yan Pei ◽  
Jun Yu ◽  
Hideyuki Takagi
Keyword(s):  
Parameter Space ◽  
Statistical Tests ◽  
Distribution Characteristic ◽  
Multi Objective Optimization ◽  
Pareto Improvement ◽  
Pareto Solutions ◽  
Pareto Solution ◽  
Multi Objective ◽  
Objective Space ◽  
Convergence Point

We propose a method to accelerate evolutionary multi-objective optimization (EMO) search using an estimated convergence point. Pareto improvement from the last generation to the current generation supports information of promising Pareto solution areas in both an objective space and a parameter space. We use this information to construct a set of moving vectors and estimate a non-dominated Pareto point from these moving vectors. In this work, we attempt to use different methods for constructing moving vectors, and use the convergence point estimated by using the moving vectors to accelerate EMO search. From our evaluation results, we found that the landscape of Pareto improvement has a uni-modal distribution characteristic in an objective space, and has a multi-modal distribution characteristic in a parameter space. Our proposed method can enhance EMO search when the landscape of Pareto improvement has a uni-modal distribution characteristic in a parameter space, and by chance also does that when landscape of Pareto improvement has a multi-modal distribution characteristic in a parameter space. The proposed methods can not only obtain more Pareto solutions compared with the conventional non-dominant sorting genetic algorithm (NSGA)-II algorithm, but can also increase the diversity of Pareto solutions. This indicates that our proposed method can enhance the search capability of EMO in both Pareto dominance and solution diversity. We also found that the method of constructing moving vectors is a primary issue for the success of our proposed method. We analyze and discuss this method with several evaluation metrics and statistical tests. The proposed method has potential to enhance EMO embedding deterministic learning methods in stochastic optimization algorithms.

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