Analog Fault Identification in RF Circuits using Artificial Neural Networks and Constrained Parameter Extraction

The increase of analog and mixed-signal circuitry in modern RF and microwave integrated circuits demands for improved analog fault diagnosis methods. While digital fault diagnosis is well established, the analog counterpart is relatively much less mature due to the intrinsic complexity in analog faults and their corresponding identification. In this work, we present an artificial neural network (ANN) modeling approach to efficiently emulate the injection of analog faults in RF circuits. The resulting meta-model is used for fault identification by applying an optimization-based process using a constrained parameter extraction formulation. The proposed methodology is illustrated by a faulty analog CMOS RF circuit

Analog Gross Fault Identification in RF Circuits using Neural Models and Constrained Parameter Extraction

The demand and relevance of efficient analog fault diagnosis methods for modern RF and microwave integrated circuits increases with the growing need and complexity of analog and mixed-signal circuitry. The well-established digital fault diagnosis methods are insufficient for analog circuitry due to the intrinsic complexity in analog faults and their corresponding identification process. In this work, we present an artificial neural network (ANN) modeling approach to efficiently emulate the injection of analog faults in RF circuits. The resulting meta-model is used for fault identification by applying an optimization-based process using a constrained parameter extraction formulation. A generalized neural modeling formulation to include auxiliary measurements in the circuit is proposed. This generalized formulation significantly increases the uniqueness of the faults identification process. The proposed methodology is illustrated by two faulty analog circuits: a CMOS RF voltage amplifier and a reconfigurable bandpass microstrip filter

Time-efficient fault detection and diagnosis system for analog circuits

Time-efficient fault analysis and diagnosis of analog circuits are the most important prerequisites to achieve online health monitoring of electronic equipments, which are involving continuing challenges of ultra-large-scale integration, component tolerance, limited test points but multiple faults. This work reports an FPGA (field programmable gate array)-based analog fault diagnostic system by applying two-dimensional information fusion, two-port network analysis and interval math theory. The proposed system has three advantages over traditional ones. First, it possesses high processing speed and smart circuit size as the embedded algorithms execute parallel on FPGA. Second, the hardware structure has a good compatibility with other diagnostic algorithms. Third, the equipped Ethernet interface enhances its flexibility for remote monitoring and controlling. The experimental results obtained from two realistic example circuits indicate that the proposed methodology had yielded competitive performance in both diagnosis accuracy and time-effectiveness, with about 96% accuracy while within 60 ms computational time.Peer reviewedFinal Published versio

Automatic programming methodologies for electronic hardware fault monitoring

This paper presents three variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of electronic circuits can be best performed by the Stressor - susceptibility interaction model. A circuit or a system is considered to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after pre-processing and standardization are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm and classification and regression trees. The performance of the proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for future fault monitoring systems.This research was supported by the International Joint Research Grant of the IITA (Institute of Information Technology Assessment) foreign professor invitation program of the MIC (Ministry of Information and Communication), Korea

Real-Time Fault Detection and Diagnosis System for Analog and Mixed-Signal Circuits of Acousto-Magnetic EAS Devices

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The paper discusses fault diagnosis of the electronic circuit board, part of acousto-magnetic electronic article surveillance detection devices. The aim is that the end-user can run the fault diagnosis in real time using a portable FPGA-based platform so as to gain insight into the failures that have occurred.Peer reviewe

Oscillation-based DFT for Second-order Bandpass OTA-C Filters

This document is the Accepted Manuscript version. Under embargo until 6 September 2018. The final publication is available at Springer via https://doi.org/10.1007/s00034-017-0648-9.This paper describes a design for testability technique for second-order bandpass operational transconductance amplifier and capacitor filters using an oscillation-based test topology. The oscillation-based test structure is a vectorless output test strategy easily extendable to built-in self-test. The proposed methodology converts filter under test into a quadrature oscillator using very simple techniques and measures the output frequency. Using feedback loops with nonlinear block, the filter-to-oscillator conversion techniques easily convert the bandpass OTA-C filter into an oscillator. With a minimum number of extra components, the proposed scheme requires a negligible area overhead. The validity of the proposed method has been verified using comparison between faulty and fault-free simulation results of Tow-Thomas and KHN OTA-C filters. Simulation results in 0.25μm CMOS technology show that the proposed oscillation-based test strategy for OTA-C filters is suitable for catastrophic and parametric faults testing and also effective in detecting single and multiple faults with high fault coverage.Peer reviewedFinal Accepted Versio

Measurements, Models, Systems and Design

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Analog circuit fault diagnosis via FOA-LSSVM

At present, the research on fault detection and diagnosis technology is very significant to improve the reliability of the equipment, which can greatly improve the safety and efficiency of the equipment. This paper proposes a new fault detection and diagnosis means based on the FOA-LSSVM algorithm. Experimental results demonstrate that the algorithm is effective for the detection and diagnosis of analog circuit faults. In addition, the model also demonstrate good generalization ability