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Fuzzy logic technique for accurate analog circuits macromodel sizing

机译:精确模拟电路宏模型尺寸的模糊逻辑技术

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Design 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.
机译:模拟集成电路的设计需要在模拟设计层次结构的低层进行大量仿真。仿真会增加总体设计时间,因此,混合ASIC的模拟部分需要大部分设计时间,而只使用一小部分硅芯片。为了提高这些模拟的效率,有必要使用基于层次描述的方法和工具,其中宏模型起着至关重要的作用。本文提出了一种将模糊逻辑应用于精确的模拟电路宏模型尺寸调整的方法。在我们提出的方法中,训练了多个自适应神经模糊推理系统来预测CMOS模拟电路的性能特征(增益,带宽)。而且,所提出的方法提供了可重用的宏模型,因为它适用于大量和大范围的设计参数。这项技术适用于全差分伸缩式运算跨导放大器宏模型的精确尺寸确定,该模型广泛用于高性能模数转换器的设计中。与从设备级SPICE仿真获得的结果相比,神经模糊计算的特征值具有极好的一致性,并且快一个数量级。此外,与其他经典技术(例如多项式回归,样条插值或人工神经网络)相比,该方法提供了最佳准确性。

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