Fully- and Quasi-Vertical GaN-on-Si p-i-n Diodes: High Performance and Comprehensive Comparison

2017 ◽  
Vol 64 (3) ◽  
pp. 809-815 ◽  
Author(s):  
Xu Zhang ◽  
Xinbo Zou ◽  
Xing Lu ◽  
Chak Wah Tang ◽  
Kei May Lau
2017 ◽  
Vol 38 (2) ◽  
pp. 248-251 ◽  
Author(s):  
Yuhao Zhang ◽  
Daniel Piedra ◽  
Min Sun ◽  
Jonas Hennig ◽  
Armin Dadgar ◽  
...  
Keyword(s):  

2020 ◽  
Vol 69 ◽  
pp. 1421-1471
Author(s):  
Aristotelis Lazaridis ◽  
Anestis Fachantidis ◽  
Ioannis Vlahavas

Deep Reinforcement Learning is a topic that has gained a lot of attention recently, due to the unprecedented achievements and remarkable performance of such algorithms in various benchmark tests and environmental setups. The power of such methods comes from the combination of an already established and strong field of Deep Learning, with the unique nature of Reinforcement Learning methods. It is, however, deemed necessary to provide a compact, accurate and comparable view of these methods and their results for the means of gaining valuable technical and practical insights. In this work we gather the essential methods related to Deep Reinforcement Learning, extracting common property structures for three complementary core categories: a) Model-Free, b) Model-Based and c) Modular algorithms. For each category, we present, analyze and compare state-of-the-art Deep Reinforcement Learning algorithms that achieve high performance in various environments and tackle challenging problems in complex and demanding tasks. In order to give a compact and practical overview of their differences, we present comprehensive comparison figures and tables, produced by reported performances of the algorithms under two popular simulation platforms: the Atari Learning Environment and the MuJoCo physics simulation platform. We discuss the key differences of the various kinds of algorithms, indicate their potential and limitations, as well as provide insights to researchers regarding future directions of the field.


2015 ◽  
Vol 62 (3) ◽  
pp. 776-781 ◽  
Author(s):  
Qi Zhou ◽  
Bowen Chen ◽  
Yang Jin ◽  
Sen Huang ◽  
Ke Wei ◽  
...  

2019 ◽  
Vol 97 (7) ◽  
pp. 546-554 ◽  
Author(s):  
Elham Mokhtarzadeh ◽  
Jafar Abolhasani ◽  
Javad Hassanzadeh

Introducing novel mimic materials as alternatives for natural enzymes challenges the analysts. Study on the peroxidase-like materials is an active field in analytical research areas. Herein, Au/Cu bimetal nanoclusters (Au/Cu NCs) are introduced as highly efficient peroxidase mimics, which were investigated using fluorometric and colorimetric techniques. A comprehensive comparison between the catalytic activity of Au, Cu, and their bimetal NCs, with different ratios of Au/Cu was performed using some different peroxidase substrates (including 3,3′,5,5′-tetramethylbenzidine (TMB), o-phenylenediamine dihydrochloride (OPD), and terephthalic acid (TA)). Additionally, different capping agents were applied for the synthesis of NCs, and it was found that penicillamine-capped NCs with 50% Cu have higher activity than other synthesized NCs. Analytical application of the novel mimic for H2O2 detection caused a linear calibration in a wide linear range of 0.001–3 μmol/L, and a great detection limit (3S) of 0.18 nmol/L, using a sensitive fluorescence system. The developed system was also sensitive for recognizing glucose and cholesterol in blood samples, after their enzymatic oxidation and production of H2O2. Detection limits of 55 and 15 nmol/L were obtained for glucose and cholesterol, respectively. The presented method also showed good reliability, which was validated by certified reference materials.


2018 ◽  
Vol 65 (1) ◽  
pp. 207-214 ◽  
Author(s):  
Sen Huang ◽  
Xinyu Liu ◽  
Xinhua Wang ◽  
Xuanwu Kang ◽  
Jinhan Zhang ◽  
...  

2008 ◽  
Vol 573-574 ◽  
pp. 153-163
Author(s):  
Martin Trentzsch ◽  
Christian Golz ◽  
Karsten Wieczorek ◽  
Rolf Stephan ◽  
Tilo Mantei ◽  
...  

In this work we present a comprehensive comparison of ultra thin thermally nitrided (TN) to plasma nitrided (PN) gate dielectrics (GD). We will show that thermal nitridation is a promising technique to increase the nitrogen concentration up to 25%. Furthermore, we will demonstrate that ultra thin thermally nitrided GD have the potential to be an alternative solution compared to plasma nitrided GD. This work includes the analysis of physical and electrical parameters as well as reliability results from reliability characterization. Additionally, we investigated the impact of Deuterium on electrical parameters and reliability behavior.


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