scholarly journals Machine learning for predictive electrical performance using OCD (Erratum)

2019 ◽  
Author(s):  
Sayantan Das ◽  
Joey Hung ◽  
Sandip Halder ◽  
Guillaume Schelcher ◽  
Roy Koret ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electrical Performance

Machine learning for predictive electrical performance using OCD

2019 ◽  
Author(s):  
Sayantan Das ◽  
Joey Hung ◽  
Sandip Halder ◽  
Guillaume Schelcher ◽  
Roy Koret ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electrical Performance

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Metal Microstructures in Advanced CMOS Devices

1996 ◽  
Vol 54 ◽  
pp. 358-359
Author(s):  
L. M. Gignac ◽  
K. P. Rodbell
Keyword(s):  
Grain Boundaries ◽  
Semiconductor Device ◽  
Chemical Vapor ◽  
Microstructural Characterization ◽  
Metal Films ◽  
Thin Metal ◽  
Electrical Performance ◽  
Thin Metal Films ◽  
Chemical Vapor Deposited ◽  
Boundary Properties

As advanced semiconductor device features shrink, grain boundaries and interfaces become increasingly more important to the properties of thin metal films. With film thicknesses decreasing to the range of 10 nm and the corresponding features also decreasing to sub-micrometer sizes, interface and grain boundary properties become dominant. In this regime the details of the surfaces and grain boundaries dictate the interactions between film layers and the subsequent electrical properties. Therefore it is necessary to accurately characterize these materials on the proper length scale in order to first understand and then to improve the device effectiveness. In this talk we will examine the importance of microstructural characterization of thin metal films used in semiconductor devices and show how microstructure can influence the electrical performance. Specifically, we will review Co and Ti silicides for silicon contact and gate conductor applications, Ti/TiN liner films used for adhesion and diffusion barriers in chemical vapor deposited (CVD) tungsten vertical wiring (vias) and Ti/AlCu/Ti-TiN films used as planar interconnect metal lines.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning
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