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Application of Machine Learning in D...

Dave, Abhilasha Harsukhbhai.

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Application of Machine Learning in Digital Logic Circuit Design Verification and Testing.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Application of Machine Learning in Digital Logic Circuit Design Verification and Testing./
Author:	Dave, Abhilasha Harsukhbhai.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	104 p.
Notes:	Source: Masters Abstracts International, Volume: 79-12.
Contained By:	Masters Abstracts International79-12.
Subject:	Computer Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10824322
ISBN:	9780438068674

Application of Machine Learning in Digital Logic Circuit Design Verification and Testing.
Dave, Abhilasha Harsukhbhai.

Application of Machine Learning in Digital Logic Circuit Design Verification and Testing. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 104 p.

Source: Masters Abstracts International, Volume: 79-12.

Thesis (M.S.)--California State University, Fresno, 2018.

This item must not be sold to any third party vendors.

Test pattern generation and fault simulation portray the essential role for the structural testing of the Integrated Chips. Structural testing validates the correctness of a circuit in terms of gates and interconnection between gates. The primary role of structural testing is to simulate the various operations of the circuit. To simulate the circuits for the structural testing several Electronic Design Automation (EDA) tools are available for fault detection and test patterns generation. This thesis presents a new approach for fault detection and test pattern generation in the combinational circuit by using machine learning techniques. The Machine-learning model can be trained for predicting the behavioral architecture of the circuit. This machine learning model can predict the test patterns and number of possible faults by giving the inputs such as primary input and number of gates required for the circuit. In addition, a truth table of a design can be used by machine learning model to verify the functionality of a given circuit. The main purpose of this research work is to use machine learning to develop a new approach for VLSI testing and design verification of a digital logic circuit.

ISBN: 9780438068674Subjects--Topical Terms:

1567821
Computer Engineering.

Application of Machine Learning in Digital Logic Circuit Design Verification and Testing.
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100 1 $a Dave, Abhilasha Harsukhbhai. $3 3541591
245 1 0 $a Application of Machine Learning in Digital Logic Circuit Design Verification and Testing.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 104 p.
500 $a Source: Masters Abstracts International, Volume: 79-12.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Raeisi, Reza.
502 $a Thesis (M.S.)--California State University, Fresno, 2018.
506 $a This item must not be sold to any third party vendors.
520 $a Test pattern generation and fault simulation portray the essential role for the structural testing of the Integrated Chips. Structural testing validates the correctness of a circuit in terms of gates and interconnection between gates. The primary role of structural testing is to simulate the various operations of the circuit. To simulate the circuits for the structural testing several Electronic Design Automation (EDA) tools are available for fault detection and test patterns generation. This thesis presents a new approach for fault detection and test pattern generation in the combinational circuit by using machine learning techniques. The Machine-learning model can be trained for predicting the behavioral architecture of the circuit. This machine learning model can predict the test patterns and number of possible faults by giving the inputs such as primary input and number of gates required for the circuit. In addition, a truth table of a design can be used by machine learning model to verify the functionality of a given circuit. The main purpose of this research work is to use machine learning to develop a new approach for VLSI testing and design verification of a digital logic circuit.
590 $a School code: 6050.
650 4 $a Computer Engineering. $3 1567821
690 $a 0464
710 2 $a California State University, Fresno. $b Electrical and Computer Engineering. $3 3431957
773 0 $t Masters Abstracts International $g 79-12.
790 $a 6050
791 $a M.S.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10824322