Spice-compatible nonlinear Op Amp and CFOA macro-models
This paper describes SPICE-compatible macromodels to model the nonlinear behavior of operational amplifiers (Op Amp) and current feedback operational amplifiers (CFOA). The proposed macro-models include not only those performance parameters more important of the Op Amp and CFOA like the dynamic rang...
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Published in | 2014 IEEE Central America and Panama Convention (CONCAPAN XXXIV) pp. 1 - 4 |
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Main Authors | , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.11.2014
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Subjects | |
Online Access | Get full text |
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Summary: | This paper describes SPICE-compatible macromodels to model the nonlinear behavior of operational amplifiers (Op Amp) and current feedback operational amplifiers (CFOA). The proposed macro-models include not only those performance parameters more important of the Op Amp and CFOA like the dynamic range, slew-rate, DC gain and gain-bandwidth product, but parasitic resistors and capacitors associated to the input-output terminals are also included. To validate the proposed macro-models, non-inverting amplifiers and one nonlinear resistor called saturated nonlinear function series (SNFS) were built to be used as test vehicles. First, the UA741 and AD844AN foundry-provided macro-models were used for generating frequency-domain numerical simulations of each no-inverting amplifier. Simulation results were compared with numerical results obtained by means of the proposed macro-models, showing a good accuracy among them. Afterwards, a chaotic signal experimentally obtained was applied as excitation signal for each SNFS built with the foundry-provided macro-models and with the proposed macro-models, confirming good agreement among theoretical simulations and experimental tests. Our results indicate that the proposed macro-models can be used either frequency- or time-domain for forecasting the behavior of linear and nonlinear circuits without that the accuracy worsens and in a less time compared with the foundry-provided macro-models. |
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DOI: | 10.1109/CONCAPAN.2014.7000420 |