SFG Based Fault Simulation of Linear Analog Circuits Using Fault Classification and Sensitivity Analysis

Bhattacharya, Rahul; Ragamai, S. H. M.; Kumar, Somesh

doi:10.1007/978-981-10-7470-7_19

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…In [2], authors proposed a flow for more efficient simulation of defects, consistent with IEEE P2427 draft standard. A MATLAB/Simulink-based framework for fault simulation of linear analog circuits has been proposed in [67]. The simulation started by identifying the type of defect and then constructing a Signal Flow Graph (SFG) of the corresponding faulty circuit.…”

Section: J Commercial Tools For Defect Injection and Simulationmentioning

confidence: 99%

Analog Defect Injection and Fault Simulation Techniques: A Systematic Literature Review

Azam

Dall'Ora

Fraccaroli

et al. 2024

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Since the last century, the exponential growth of the semiconductor industry has led to the creation of tiny and complex integrated circuits, e.g., sensors, actuators, and smart power. Innovative techniques are needed to ensure the correct functionality of analog devices that are ubiquitous in every smart system. The ISO 26262 standard for functional safety in the automotive context specifies that fault injection is necessary to validate all electronic devices. For decades, standardization of defect modeling and injection mainly focused on digital circuits and, in a minor part, on analog ones. An initial attempt is being made with the IEEE P2427 draft standard that started to give a structured and formal organization to the analog testing field.Various methods have been proposed in the literature to speed up the fault simulation of the defect universe for an analog circuit. A more limited number of papers seek to reduce the overall simulation time by reducing the number of defects to be simulated. This literature survey describes the state-of-the-art of analog defect injection and fault simulation methods. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing for a systematic and complete literature survey. Each selected paper has been categorized and presented to provide an overview of all the available approaches. In addition, the limitations of the various approaches are discussed by showing possible future directions.

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Section: J Commercial Tools For Defect Injection and Simulationmentioning

confidence: 99%

Analog Defect Injection and Fault Simulation Techniques: A Systematic Literature Review

Azam

Dall'Ora

Fraccaroli

et al. 2024

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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“…In [ 18 ], an approach for fault diagnosis in analog circuits is presented, using MATLAB/Simulink modeling functionalities. The circuits are modeled in two scenarios: with and without faults.…”

Section: Related Workmentioning

confidence: 99%

Fault Diagnosis in Analog Circuits Using Swarm Intelligence

Nedjah,

Galindo,

Mourelle

et al. 2023

Biomimetics

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Open or short-circuit faults, as well as discrete parameter faults, are the most commonly used models in the simulation prior to testing methodology. However, since analog circuits exhibit continuous responses to input signals, faults in specific circuit elements may not fully capture all potential component faults. Consequently, diagnosing faults in analog circuits requires three key aspects: identifying faulty components, determining faulty element values, and considering circuit tolerance constraints. To tackle this problem, a methodology is proposed and implemented for fault diagnosis using swarm intelligence. The investigated optimization techniques are Particle Swarm Optimization (PSO) and the Bat Algorithm (BA). In this methodology, the nonlinear equations of the tested circuit are employed to calculate its parameters. The primary objective is to identify the specific circuit component that could potentially exhibit the fault by comparing the responses obtained from the actual circuit and the responses obtained through the optimization process. Two circuits are used as case studies to evaluate the performance of the proposed methodologies: the Tow–Thomas Biquad filter (case study 1) and the Butterworth filter (case study 2). The proposed methodologies are able to identify or at least reduce the number of possible faulty components. Four main performance metrics are extracted: accuracy, precision, sensitivity, and specificity. The BA technique demonstrates superior performance by utilizing the maximum combination of accessible nodes in the tested circuit, with an average accuracy of 95.5%, while PSO achieved only 93.9%. Additionally, the BA technique outperforms in terms of execution time, with an average time reduction of 7.95% reduction for the faultless circuit and an 8.12% reduction for the faulty cases. Compared to the machine-learning-based approach, using BA with the proposed methodology achieves similar accuracy rates but does not require any datasets nor any time-demanding training to proceed with circuit diagnostic.

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