V. Litovski

Analogue electronic circuit diagnosis based on ANNs

Feed-forward artificial neural networks (ANNs) have been applied to the diagnosis of nonlinear dynamic analogue electronic circuits. Using the simulation-before-test (SBT) approach, a fault dictionary was first created containing responses observed at all inputs and outputs of the circuit. The ANN was considered as an approximation algorithm to capture mapping enclosed within the fault dictionary and, in addition, as an algorithm for searching the fault dictionary in the diagnostic phase. In the example given DC and small signal frequency domain measurements were taken as these data are usually given in device’s data-sheets. A reduced set of data per fault (DC output values, the nominal gain and the 3 dB cut-off frequency, measured at one output terminal) was recorded. Soft (parametric) and catastrophic (shorts and opens) defects were introduced and diagnosed simultaneously and successfully. Large representative set of faults was considered, i.e., all possible catastrophic transistor faults and qualified representatives of soft transistor faults were diagnosed in an integrated circuit. The generalization property of the ANNs was exploited to handle noisy measurement signals.

POWER FACTOR AND DISTORTION MEASURING FOR SMALL LOADS USING USB ACQUISITION MODULE

Power factor and distortion measuring usually require dedicated and expensive equipments. Computer-based acquisition modules and software provide for a possibility to create simple and nonexpensive methods and instruments for power factor measurement and distortion characterization of small loads and bring all advantages of virtual instrumentation. A new approach to power quality characterization by measuring power factor, distortion, and several other parameters of small electric loads (up to 0.5 kW) will be described in this paper. Besides low price maximum versatility and adaptability are provided without any loss in accuracy.

Mixed-signal modeling with AleC++: Specific features of the HDL

Abstract Alecsis behavioral simulator and its mixed-signal hardware description language (HDL) AleC++ form an open simulation environment, where electronic circuits and non-electrical systems can be analyzed. This paper describes some of the features of AleC++ language, namely mainly those that are not in accordance with the IEEE standard for VHDL and VHDL-AMS, but are very useful for modeling complex systems.

Fault location in passive analog RC circuits by measuring impulse response

In this paper modified impulse response estimation method is described. After recalling the general problems of automatic symbolic function generation, and a basis of impulse response estimation method, expanding of this method is proposed. This paper deals with fault location of single fault in analog electronic circuits. In the paper, an example of this approach is given. Finally, the utility of this method when two faults occured in a circuit is considered.

Fault models of CMOS circuits

Abstract A systematic procedure for fault modelling of CMOS circuits is described. It starts with the physical fault and produces a set of tables describing the logic behaviour of the gate. This set of tables is referred to as the fault model and includes truth tables, fault equivalence tables, fault coverage tables, and fault propagation tables. Starting with the procedure of fault modelling of simple combinational circuits, a method is advised for model generation of complex sequential structures. Application of fault modelling for yield evaluation and test pattern generation is considered, too.

Simulation of non-linear magnetic circuits modelled using artificial neural network

Abstract Simulation of electromechanical systems and other systems where coupling of different physical effects is modelled, is currently a very active research area. It gives rise to development of hardware description languages where description of such different physical devices is possible and to implementation of simulators that support such languages. Models of devices for system simulation should be simple enough, so that simulation time stays in reasonable limits, but must describe correctly enough all relevant physical dependencies. Modelling with such demands is a very difficult task. An automated approach for solving this problem is usage of neural networks for modelling. In this paper, a neural network is used to model some complex physical dependencies of an electromagnetic circuit, while dynamic mechanical behaviour is modelled analytically. Model is described in an object-oriented hardware description language and simulated by the simulator Alecsis. In this way, a fast and accurate simulation of electromagnetic system is obtained.

Procedural analog design (PAD) tool

This paper presents a new procedural analog design tool called PAD. It is a chart-based design environment dedicated to the design of analog circuits aiming to optimise design and quality by finding good tradeoffs. This interactive tool allows step-by-step design of analog cells by using guidelines for each analog topology. At each step, the user modifies interactively one subset of design parameters and observes the effect on other circuit parameters. At the end, an optimised design is ready for simulation (verification and fine-tuning). Furthermore, PAD provides a layout generator for matched substructures such as current mirror, cascode stage, differential pair, etc. The analog basic structures calculator embedded in PAD uses the complete set of equations of the EKV MOS model, which links the equations for weak and strong inversion in a continuous way. The present version of PAD covers the procedural design of transconductance amplifiers (OTAs) and different operational amplifiers topologies.

Comparison of some ANN based forecasting methods implemented on short time series

Several ANN architectures were implemented for forecasting based on reduced time series encountered in the subject of waste management of electrical and electronic products. Along with the existing ones new original training set organization and ANNpsilas structures are proposed that perform favorably comparing with the existing ones.

Fault Diagnosis in Digital Part of Sigma-Delta Converter

In this paper the artificial neural network (ANN) is applied to diagnosis of defects in the digital part of a nonlinear mixed-mode circuit. Both catastrophic and delay defects are considered. The approach is demonstrated on the example of a relatively complex sigma-delta modulator. Delay defects in this example are delays of rising and falling edge of digital signals and catastrophic defects are considered as stuck switches. Fault dictionary is created, by simulation, using the response of the circuit to an input ramp signal. It is represented in a form of a look-up table. Artificial neural network is then trained for modeling (memorizing) the look-up table. The diagnosis is performed so that the ANN is excited by faulty responses in order to present the fault codes at its output. There were no errors in identifying the faults during diagnosis

Behavioural modelling, simulation, test and diagnosis of MEMS using ANNs

The design of micro-electrical-mechanical systems requires that the entire system can be modelled and simulated. Additionally, behaviour under fault conditions must be simulated to determine test and diagnosis strategies. While the electrical parts of a system can be modelled at transistor, gate or behavioural levels, the mechanical parts are conventionally modelled in terms of partial differential equations (PDEs). Mixed-signal electrical simulations are possible, using e.g. VHDL-AMS, but simulations that include PDEs are prohibitively expensive. Here, we show that complex PDEs can be replaced by black-box functional models and, importantly, such models can be characterized automatically and rapidly using artificial neural networks (ANNs). We demonstrate a significant increase in simulation speed and show that test and diagnosis strategies can be derived using such models.