Prediction of Electrical and Physical Failure Analysis Success Using Artificial Neural Networks

Zhao, Lin; Goh, S.H.; Chan, Yi‐Hsin; Yeoh, B.L.; Hu, Hao; Thor, MH; Tan, Alan; Lam, Jeffrey

doi:10.1109/ipfa.2018.8452176

Cited by 13 publications

(3 citation statements)

References 7 publications

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“…Traditionally, defects are located using a physical‐level process called physical failure analysis (PFA) (S.‐Y. Liu, Hou, Chang, & Lin, 2013; Zhao et al, 2018). However, the intricacy and multitude of defects have not only made this process challenging but also very time consuming and costly.…”

Section: Yield Learning and Diagnosismentioning

confidence: 99%

A survey of digital circuit testing in the light of machine learning

Pradhan

Bhattacharya

2020

WIREs Data Min & Knowl

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The insistent trend in today's nanoscale technology, to keep abreast of the Moore's law, has been continually opening up newer challenges to circuit designers. With rapid downscaling of integration, the intricacies involved in the manufacturing process have escalated significantly. Concomitantly, the nature of defects in silicon chips has become more complex and unpredictable, adding further difficulty in circuit testing and diagnosis. The volume of test data has surged and the parameters that govern testing of integrated circuits have increased not only in dimension but also in the complexity of their correlation. Evidently, the current scenario serves as a pertinent platform to explore new test solutions based on machine learning. In this survey, we look at various recent advances in this evolving domain in the context of digital logic testing and diagnosis. This article is categorized under: Algorithmic Development > Structure Discovery Technologies > Machine Learning Technologies > Prediction

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Section: Yield Learning and Diagnosismentioning

confidence: 99%

A survey of digital circuit testing in the light of machine learning

Pradhan

Bhattacharya

2020

WIREs Data Min & Knowl

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“…Studies of such situations have confirmed that ensuring the stability of the voltage increases reliability, supporting the growth and development in electric power systems driven by the increasing use of modern technology that has increased the need for high-pressure systems, however, offering new techniques to stabilise the transient states of power systems [2], [3]. Several layers' performance can be compared using AI probability techniques or methods; such probability is developed within an artificial neural network (ANN) toolbox, and the resulting systems can be digitally analysed to assess the control stability of the linear single-loop voltage, R, in each case [4] in order to determine critical frequency that defines the stability region of R. Artificial intelligence can thus also be applied to find effective solutions to stabilising the voltage in electrical power systems using limited data [5]. The principal goal for such work is to build monitoring system which can assess all changes that occur to the network by monitoring states of both stability and instability, as such a system takes into account all the malfunctions that do occur and all those that will occur to offer detailed and satisfactory analytical results [6].…”

Section: Introductionmentioning

confidence: 99%

Voltage stability assessment prediction using a guide strategy-based adaptive particle swarm optimisation-neural network algorithm

Hussein

Naji

Salman

2022

IJPEDS

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In this work, the indicators of electrical power network stability and voltage stability (VS) are discussed and developed with the aim of using a power transfer stability index (PTSI) indicator as a predictor for voltage stability (VS) in electrical power networks. The power transfer stability index (PTSI) was thus used to detect abnormally weak voltages in buses within such power system networks (weak). The target data are obtained using the Newton Raphson method (NR) and include magnitude, phase angle, and active and reactive power. A new adaptive particle swarm optimization-neural network algorithm based on a guiding strategy (GSAPSO-NN) was also used to achieve the goal of the paper by improving the mixed particle updates and the weightings of the neural network to decrease the search time. All results were then compared with actual values as calculated using the PTSI NR method. The final results show only simple differences or approximately the same values using both the proposed and the classical methods. The MATLAB-PSAT package was employed to obtain most of these results and the testing of the new method was done on the IEEE14 bus system as well as the Iraqi 24-bus power system. The effectiveness validation of the new hybrid method for assessing voltage stability was thus achieved.

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“…The establishment of the minimal input data set required for the monitoring and assessment of voltage stability is the ultimate purpose of this research. A method that considers the cascading failure of power systems to analysis their angle stability is proposed in [6]. The transfer probability between the elements in the set is calculated by applying the discrete Markov theory to define the cascading failure process thus establishing the system's operating condition set taking into account stochastic events based on the flow transfer theory.…”

Section: Introductionmentioning

confidence: 99%

Assessment of voltage stability based on power transfer stability index using computational intelligence models

Ghadban

Salman

Hussein

2021

IJECE

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In this paper, the importance of voltage stability is explained, which is a great problem in the EPS. The estimation of VS is made a priority so as to make the power system stable and prevent it from reaching voltage collapse. The power transfer stability index (PTSI) is used as a predictor utilized in a PSN to detect the instability of voltages on weakened buses. A PSI is used to obtain a voltage assessment of the PSNs. Two hybrid algorithms are developed. The (CA-NN) and the (PSO-NN). After developing algorithms, they are compared with the actual values of PTSI NR method. The algorithms installed on the 24 bus Iraqi PS. The actual values of PTSI are the targets needed. They are obtained from the NR algorithm when the input data is Vi, δi, Pd, Qd for the algorithm. The results indicate that a weak bus that approaches voltage collapse and all results were approximately the same. There is a slight difference with the actual results and demonstrated classical methods are slower and less accurate than the hybrid algorithms. It also demonstrates the validation and effectiveness of algorithms (CA-NN, and PSO-NN) for assessing voltage-prioritizing algorithms (CA-NN). The MATLAB utilized to obtain most of the results.

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Prediction of Electrical and Physical Failure Analysis Success Using Artificial Neural Networks

Cited by 13 publications

References 7 publications

A survey of digital circuit testing in the light of machine learning

A survey of digital circuit testing in the light of machine learning

Voltage stability assessment prediction using a guide strategy-based adaptive particle swarm optimisation-neural network algorithm

Assessment of voltage stability based on power transfer stability index using computational intelligence models

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