scholarly journals Maximum Entropy Principle within A Total Energy Scheme for Hot-carrier Transport in Semiconductor Devices

VLSI Design ◽  
2001 ◽  
Vol 13 (1-4) ◽  
pp. 381-386 ◽  
Author(s):  
M. Trovato ◽  
L. Reggiani
Keyword(s):  
Maximum Entropy ◽  
Carrier Transport ◽  
Maximum Entropy Principle ◽  
Average Energy ◽  
Band Model ◽  
Hot Carrier ◽  
Hydrodynamic Calculations ◽  
Entropy Principle ◽  
The Moment ◽  
Total Average

By extending the maximum entropy principle within a scheme in total average energy we obtain a closed system of hydrodynamic equations for a full nonparabolic band model in which all the unknown constitutive functions are completely determined. The theory is validated by comparing hydrodynamic calculations with Monte Carlo simulations performed for bulk and submicron Si structures at 300 K. In the general framework of the moment theory a systematic study of small-signal response functions is provided.

scholarly journals Moment Equations with Maximum Entropy Closure for Carrier Transport in Semiconductor Devices: Validation in Bulk Silicon

VLSI Design ◽  
2000 ◽  
Vol 10 (4) ◽  
pp. 335-354 ◽  
Author(s):  
A. M. Anile ◽  
O. Muscato ◽  
V. Romano
Keyword(s):  
Maximum Entropy ◽  
Moment Method ◽  
Carrier Transport ◽  
Maximum Entropy Principle ◽  
Bulk Silicon ◽  
Moment Equations ◽  
Balance Equations ◽  
Entropy Principle ◽  
Closure Relations ◽  
The Moment

Balance equations based on the moment method for the transport of electrons in silicon semiconductors are presented. The energy band is assumed to be described by the Kane dispersion relation. The closure relations have been obtained by employing the maximum entropy principle.The validity of the constitutive equations for fluxes and production terms of the balance equations has been checked with a comparison to detailed Monte Carlo simulations in the case of bulk silicon.

The Evolution of Complex Systems

1988 ◽  
Vol 43 (1) ◽  
pp. 73-77
Author(s):  
G. L. Hofacker ◽  
R. D. Levine
Keyword(s):  
Complex Systems ◽  
Maximum Entropy ◽  
Transition Probabilities ◽  
Maximum Entropy Principle ◽  
Average Energy ◽  
Entropy Principle ◽  
Fundamental Equations ◽  
Special Case ◽  
Extremal Properties

Abstract A principle of evolution of highly complex systems is proposed. It is based on extremal properties of the information I (X, Y) characterizing two states X and Y with respect to each other, I(X, Y) = H(Y) -H(Y/X), where H(Y) is the entropy of state Y,H (Y/X) the entropy in state Y given the probability distribu­tion P(X) and transition probabilities P(Y/X).As I(X, Y) is maximal in P(Y) but minimal in P(Y/X), the extremal properties of I(X, Y) con­stitute a principle superior to the maximum entropy principle while containing the latter as a special case. The principle applies to complex systems evolving with time where fundamental equations are unknown or too difficult to solve. For the case of a system evolving from X to Y it is shown that the principle predicts a canonic distribution for a state Y with a fixed average energy .

An entropy concentration theorem: applications in artificial intelligence and descriptive statistics

1990 ◽  
Vol 27 (2) ◽  
pp. 303-313 ◽  
Author(s):  
Claudine Robert
Keyword(s):  
Artificial Intelligence ◽  
Maximum Entropy ◽  
Large Deviation ◽  
Maximum Entropy Principle ◽  
Statistical Modelling ◽  
Linear Constraints ◽  
Descriptive Statistics ◽  
Entropy Principle ◽  
Maximum Entropy Distribution ◽  
Mathematical Justification

The maximum entropy principle is used to model uncertainty by a maximum entropy distribution, subject to some appropriate linear constraints. We give an entropy concentration theorem (whose demonstration is based on large deviation techniques) which is a mathematical justification of this statistical modelling principle. Then we indicate how it can be used in artificial intelligence, and how relevant prior knowledge is provided by some classical descriptive statistical methods. It appears furthermore that the maximum entropy principle yields to a natural binding between descriptive methods and some statistical structures.

Random quantal fields and maximum entropy principle

2005 ◽  
Author(s):  
Maciej M. Duras
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Principle ◽  
Entropy Principle

SINE ENTROPY FOR UNCERTAIN VARIABLES

2013 ◽  
Vol 21 (05) ◽  
pp. 743-753 ◽  
Author(s):  
KAI YAO ◽  
JINWU GAO ◽  
WEI DAI
Keyword(s):  
Maximum Entropy ◽  
Uncertainty Theory ◽  
Maximum Entropy Principle ◽  
Expected Value ◽  
Uncertain Variables ◽  
Entropy Principle

Entropy is a measure of the uncertainty associated with a variable whose value cannot be exactly predicated. In uncertainty theory, it has been quantified so far by logarithmic entropy. However, logarithmic entropy sometimes fails to measure the uncertainty. This paper will propose another type of entropy named sine entropy as a supplement, and explore its properties. After that, the maximum entropy principle will be introduced, and the arc-cosine distributed variables will be proved to have the maximum sine entropy with given expected value and variance.

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