Analysis, modelling and optimization of a gain boosted telescopic amplifier

Aloisi, W.; Giustolisi, G.; Palumbo, G.

doi:10.1002/cta.248

Cited by 4 publications

(1 citation statement)

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“…An extensive description of such macromodels is found in [2]. These models have been developed for many different circuits, although amplifiers are usually the target circuit [5,6]. The second type is simplified subcircuits of the original circuit, which contain the dominant devices.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic technique for accurate analog circuits macromodel sizing

Díaz‐Madrid¹,

Hinojosa²,

Doménech‐Asensi³

2008

Circuit Theory & Apps

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SUMMARYDesign of analog integrated circuits requires extensive simulations at low levels of the analog design hierarchy. The simulations increase the overall design time and, thus, the analog part of a mixed ASIC requires most of the design time while using only a small part of the silicon die. To improve the efficiency of these simulations, it is necessary to use methodologies and tools based on hierarchical descriptions, where macromodels play an essential role. In this paper, an approach for applying fuzzy logic for accurate analog circuit macromodel sizing is presented. In our proposed method, multiple adaptive neuro-fuzzy inference systems are trained to predict the performance characteristics (gain, bandwidth) of CMOS analog circuits. Moreover, the presented methodology provides reusable macromodels, since it is applicable for large number and large range of design parameters. This technique is applied to the accurate sizing of fully differential telescopic operational transconductance amplifier macromodel, a circuit widely used in the design of high-performance analog-to-digital converters. The neuro-fuzzy computed characteristic values are in excellent agreement and one order of magnitude faster than those obtained from device level SPICE simulations. Moreover, this method offers the best accuracy in comparison with other classical techniques such as polynomial regression, spline interpolation or artificial neural networks.

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Section: Introductionmentioning

confidence: 99%