Bordersearch: An Adaptive Identification of Failure Regions

Dobler, M.; Harrant, Manuel; Rafaila, Monica; Pelz, Georg; Bogdan, Martin

doi:10.7873/date.2015.0249

Cited by 13 publications

(2 citation statements)

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“…possible faults) and the analysis of its impact are gaining importance. To also handle such uncertainties with discontinuities in an efficient way [26], [27] use strategies that generate parameter sets around discontinuities.…”

Section: B Worst Case Analysismentioning

confidence: 99%

Verification and validation of AMS systems: Towards coverage of uncertainties

Grimm

Radojicic

2015

2015 IEEE 20th International Mixed-Signals Testing Workshop (IMSTW)

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In today's systems, analog and mixed-signal (AMS) circuits are tightly interwoven with HW/SW systems and the application. Hence, a verification of the AMS part alone is not sufficient any more, in particular considering its application in Cyber-Physical Systems. In order to guarantee 'first time right', the accuracy of the models, the validation of the application fitness, and in changing, uncertain environments will become at least as important. In this paper we discuss and give an overview of methods that strive for validation of AMS systems with increased coverage. We in particular focus modeling, verification and validation of uncertainties in the context of Cyber-Physical Systems.

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Section: B Worst Case Analysismentioning

confidence: 99%

Verification and validation of AMS systems: Towards coverage of uncertainties

Grimm

Radojicic

2015

2015 IEEE 20th International Mixed-Signals Testing Workshop (IMSTW)

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“…However, virtual sensing is not commonly used in testing and characterizing electronic systems and components, such as in determining the failure region of a power management circuit. Furthermore, recent studies on FRD have not focused on LVR, primarily to determine the ESR tunnel graph [10], [31]. An ESR tunnel graph example that can be typically found in an LVR manufacturer's datasheet is depicted in Figure 1.…”

Section: Virtual Sensingmentioning

confidence: 99%

Failure Region Determination of Linear Voltage Regulator Using Data-driven and Model-based Virtual Sensing

Zaman¹

2019

IJATCSE

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Disturbance to the output of a linear voltage regulator (LVR) due to an abrupt change in output current can be compensated by using an output capacitor. A capacitor has an internal parasitic resistive element known as equivalent series resistance (ESR). However, the ESR value may change due to aging and temperature variations, forming a failure region in an LVR. Besides, the performance of each manufactured LVR varies due to differences in the manufacturing process. Consequently, failure region determination (FRD) for LVRs involves time-consuming and costly manual data acquisition and requires analysis to determine the failure region accurately. In this work, efficient and effective FRD methods were developed by applying the concept of virtual sensing. Two approaches were used, namely, a data-driven approach (DDA) and a model-based approach (MBA). The developed FRD methods were as follows: the data interpolation method (DDA-DIM), the input-output model-based method (MBA-IOM), and the circuit analysis model-based method (MBA-CAMM). DDA-DIM utilizes a multilayer perceptron and a radial basis function neural network. Meanwhile, MBA-IOM and MBA-CAMM estimate the black-box model of an LVR and the circuit analysis model, respectively. The results of the three methods were compared with the benchmark developed using manual FRD. MBA-CAMM was determined as the most effective and efficient FRD method that applies the virtual sensing concept.

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