【作者】 李寿林；

【导师】 高建军；

【作者基本信息】 华东师范大学 ， 无线电物理， 2011， 博士

【摘要】 随着无线通信技术的不断发展,射频集成电路在其中扮演越来越重要的角色。CMOS工艺已经成为制作可靠的射频电路最合适的工艺之一,CMOS工艺的不断进步已经使MOSFET器件具有良好的微波射频性能。相对比其它工艺,CMOS工艺还具有制造工艺成熟、成本低、功耗低、集成度高、模拟／数字兼容性好和按比例缩放特性一致等优势。为了确保所需频段的电路性能和缩短研制时间,器件模型是一个非常关键的因素。神经网络作为一种非传统建模方法已经广泛用于射频/微波计算机辅助设计。传统建模方法中的数值建模方法通常计算非常耗时；而分析方法在表征新的器件时存在较大的困难；经验模型在适用器件范围和模型精度都是有限的。相对比上述传统建模方法而言,神经网络建模方法是可供选择的有效方法,而且神经网络建模方法比传统建模方法更能适应新器件模型的开发。本论文的主要目的就是研究基于神经网络的MOSFET器件建模,主要内容包括：MOSFET器件表征及主要建模方法汇总。研究了MOSFET器件结构及其工作原理、MOSFET器件的高频特性及其表征。总结了各种MOSFET器件建模方法,重点分析了各种基于神经网络的器件建模方法。基于神经网络的MOSFET器件小信号建模技术研究。研究了MOSFET器件的测试结构及去嵌方法、小信号等效电路模型及参数提取方法。利用直接参数提取方法提取小信号模型参数并优化,建立了偏置相关的经验模型。提出一种基于神经网络-空间映射技术的MOSFET偏置相关的小信号建模方法,利用所提出的方法对130纳米工艺MOSFET器件进行小信号建模,在频率100MHz-40GHz范围内,模拟结果和测量结果吻合很好。基于神经网络的MOSFET器件直流建模技术研究。提出了两种基于神经网络-空间映射技术的MOSFET直流特性建模的方法：第一种方法利用传统的神经网络-空间映射技术对直流特性建模也称之为Neuro-SM模型；第二种方法是将传统神经网络-空间映射技术与先验知识注入及源差分法结合起来也称之为NSM-PKI-D模型。推导了上述两种方法中MOSFET器件跨导和漏导的公式。漏电流特性、跨导和漏导的模拟和测量结果比较证明了我们模型的有效性。基于神经网络的MOSFET器件大信号建模技术研究。提出了一种将等效电路和神经网络-空间映射建模技术相结合的MOSFET大信号建模方法,该方法不仅保持了模型各部分的物理含义,而且结合了神经网络-空间映射建模技术的优势。为了解决DC/AC色散问题,两个基于神经网络-空间映射的Neuro-SM模型分别用来模拟在直流和射频不同情况下的漏电流。栅长为0.13μm、单栅指的栅宽为5μm、共20个栅指的MOSFET器件的直流及各种不同偏置点下小信号和大信号模拟结果与数据的比较验证了我们模型的有效性。与经验模型的比较进一步验证了我们的模型具有更好的精度。更多还原

【Abstract】 With the continuous development of wireless communication technology, radio frequency integrated circuit plays an increasingly important role in it. Bulk CMOS technology has become one of the most feasible candidates for building reliable circuits for RF applications. The progress of CMOS technology has made MOSFET transistors show excellent microwave performance. In addition, CMOS technology has such advantages relative to other technologies as mature manufacturing process, low cost, low power, high integration, good integration with high performance digital circuits and high-speed analog circuits, and successful scalability. In order to ensure the circuit performance for the required frequency bands and also to shorten the ratio of time to market, device models are very critical. Artificial neural networks (ANNs) have already been applied to RF and Microwave computer-aided design (CAD) tasks as an unconventional alternative. Neuromodeling is efficient in comparison to conventional modeling methods, such as numerical modeling methods, which could be computationally expensive, or analytical methods, which could be difficult to obtain for new devices, or empirical models, whose range and accuracy could be limited. Furthermore, neural models are easier to develop for new devices than conventional models. Thus, neural network based MOSFET modeling is studied in this thesis. The main content of the thesis is divided into four parts as follows:Firstly, the characterization and principal modeling method for MOSFET is reviewed. The structure and operation of MOSFET is presented. The characteristic and characterization of MOSFET at high frequency is also studied. Various kinds of modeling methods for MOSFET are summarized, where we focus on neural network based modeling method.Secondly, neural network based small-signal modeling for MOSFET is studied. The test structure and de-embedding method of MOSFET are presented. Small-signal equivalent circuit and direct parameter extraction method for MOSFET are also studied. Parameters in equivalent circuit are extracted and then optimized, and corresponding bias-dependent empirical model is determined. Bias-dependent small-signal modeling approach based on neuro-space mapping is proposed for MOSFET. Good agreement is obtained between the simulated and measured results for a 130 nm MOSFET in the frequency range of 100MHz-40GHz confirming the validity and effectiveness of our approach.Thirdly, neural network based DC modeling for MOSFET is studied. Two approaches for modeling DC characteristics for MOSFET based on neuro-space mapping (SM) are proposed. The first approach makes use of classical neuro-SM technology, while the second combines neuro-SM with prior knowledge input and source difference method. The formulas for obtaining the transconductance and output conductance in two approaches are derived. TheⅠ-Ⅴcharacteristics as well as their conductances obtained by the formulas in two approaches are compared to the measured data. Experimental results, which confirm the validity of our approaches, are also presented.Finally, neural network based large-signal modeling for MOSFET is studied. A large-signal modeling approach based on the combination of equivalent circuit and neuro-space mapping modeling techniques is proposed for MOSFET. In order to account for the dispersion effects, two neuro-space mapping based models are employed to model the drain current at DC and RF conditions respectively. Corresponding training process in our approach is also presented. Good agreement is obtained between the model and data of the DC, S parameter, and harmonic performance for a 0.13μm gate length,5μm gate width per finger and 20 fingers MOSFET over a wide range of bias points, demonstrating the proposed model is valid for DC, small-signal and nonlinear operation. Comparison of DC, S-parameter and harmonic performance between proposed model and empirical model further reveals the better accuracy of the proposed model.更多还原