基于遗传算法的Kriging元模型及其在模拟集成电路优化设计中的应用

提出了建立电路Kriging元模型,并与遗传算法相结合确定电路参数,优化电路的方法.相对传统多项式回归模型,Kriging模型更适合电路仿真的实验类型;利用遗传算法,解决了基于Kriging元模型电路系统的全局优化问题.最后将该方法应用于带隙基准电路设计,取得令人满意的结果.     

试验设计与仿真相结合构造集成电路元模型的方法研究

集成电路的设计与优化常需要指标与参数简单、直接的模型,即电路的元模型.本文将试验设计与计算机仿真相结合构造集成电路的元模型.讨论了构造元模型中使用的4种试验设计类型与3种模型.结合低功耗集成运算放大器性能指标静态功耗元模型的构造,分析比较了不同方法的各自特点.从结果可以看出,将拉丁超立方抽样与Kriging模型相结合最适合仿真试验中的电路元模型的构造.     

PIN光探测器的小信号电路模型参数的提取

提出了一种利用自适应遗传算法提取p-i-n光探测器小信号电路模型参数的方法.文章首先根据p-i-n光探测器的物理结构确定其等效电路模型,进而采用自适应遗传算法对测量的S参数进行拟合,提取模型参数.自适应遗传算法自动优化交叉概率和变异概率,避免了以往遗传算法中易早熟的缺点.利用该法成功提取出模型的10个参数,建立光探测器小信号电路模型.在130 MHz～20 GHz范围内的实验结果表明,模型仿真结果和测量结果相吻合,证明了这种参数提取方法的可靠性.     

基于遗传算法的片上螺旋电感模型优化方法

给出了一种基于遗传算法的片上螺旋电感宽带电路模型优化方法。通过对螺旋电感Y参数变化规律的分析,首先给出一种能够工作在高频段的电路模型结构。对每个频段内电路模型的参数优化被认为是一个误差最小化的多目标优化问题,将具有全局搜索能力和鲁棒性能的遗传算法引入到该优化过程中。介绍了遗传算法的优化过程以及其中采用的子频带划分方法。优化结果表明,针对不同几何参数的螺旋电感,该优化方法都能得到误差在5%以内的宽频带电感模型。     

基于方程和遗传算法的CMOS模拟单元电路优化

本文在CMOS单元电路优化中采用一种基于方程和遗传算法的优化方法.其采用描述电路行为的性能解析方程和遗传算法实现电路性能指标的优化.仿真结果表明,这种方法可以得到满意的仿真结果,同时所需的优化时间较短.     

能量极小化的一种启发式遗传算法

Chakradhar et.al(1988,1990)将组合电路表示为Hopfield神经网络,将测试生成问题转化为一个组合优化问题。本文在传统遗传算法的基础上,结合电路的拓扑信息,提出了一种用于组合电路神经网络模型能量极小化的启发式遗传算法。     

基于遗传算法的开关电流存储电路的设计

针对开关电流存储电路存在固有误差和电路器件参数需要大量手工迭代计算等难题,提出基于遗传算法的开关电流存储电路设计方法.其主要思想是以Class AB栅极接地存储电路为基础,对其进行小信号模型分析,借助遗传算法对电路的电荷注入误差和时间响应性能进行多目标优化,获取电路中器件参数的最优Pareto解.采用0.5μm CMOS工艺参数,对电路进行PSPICE仿真测试.结果表明,优化设计的电路具有存储精度高、响应速度快等优点.     

基于改进遗传算法的量子可逆电路综合

针对可逆电路到量子电路的有效映射问题,提出了带禁忌表的大变异自适应遗传算法,用于量子可逆电路的综合.选取量子非门、控制非门、控制V门与控制V+门(NCV)构成量子门库,建立了量子电路计算模型.采用二进制串行编码方案,设计了适应度函数、进化算子及优化规则,实现了带禁忌表大变异自适应遗传算法的量子可逆电路综合,并用Revlib电路库进行了测试.结果表明该综合方法能同时得到多个功能解,且所生成电路的量子代价优于库中电路,验证了提出算法用于量子可逆电路综合的正确性和有效性.     

基于近似模型的电子封装散热结构优化设计

针对封装散热结构优化问题中存在的难点,提出了一种基于近似模型和随机模拟的快速全局优化方法.利用Kriging方法建立封装散热结构的近似模型,重构原始的优化问题,采用随机模拟对重构出的目标函数进行寻优,从而得到最优解.采用CVT试验设计,使Kriging模型的泛化预测能力达到最大程度的发挥;在随机模拟中采用Quasi-Monte Carlo法,有效地提高了寻优的效率.以方形扁平封装器件为例,应用该方法实现了封装散热结构的优化,结果表明所提出的方法有效地解决了设计变量中含有离散变量的问题,并且大大提高了随机模拟优化的计算效率.     

基于优化神经网络的三相全控整流电路故障诊断

研究了递阶遗传算法用于神经网络的参数确定问题,以期利用递阶遗传算法的二级编码结构解决以往同时优化神经网络结构、权值、阈值效率不高的难题。此外,建立了三相桥式全控整流电路的故障模型,并对几种常见故障进行了仿真分析。最后,将自适应递阶遗传算法用于三相桥式全控整流电路的故障诊断,仿真验证了该方法的准确性和实用性。     

Accurate metamodels of device parameters and their applications in performance modeling and optimization of analog integrated circuits

Techniques for constructing metamodels of device parameters at BSIM3v3 level accuracy are presnted to improve knowledge-based circuit sizing optimization. Based on the analysis of the prediction error of analytical performance expressions, operating point driven (OPD) metamodels of MOSFETs are introduced to capture the circuit's characteristics precisely. In the algorithm of metamodel construction, radial basis functions are adopted to interpolate the scattered multivariate data obtained from a well tailored data sampling scheme designed for MOSFETs.The OPD metamodels can be used to automatically bias the circuit at a specific DC operating point. Analytical-based performance expressions composed by the OPD metamodels show obvious improvement for most small-signal performances compared with simulation-based models. Both operating-point variables and transistor dimensions can be optimized in our nesting-loop optimization formulation to maximize design flexibility. The method is successfully applied to a low-voltage low-power amplifier.     

Polynomial metamodel based fast optimization of nano-CMOS oscillator circuits

Modern consumer electronics are designed as analog/mixed-signal systems-on-chip (AMS-SoCs). In an AMS-SoC, the analog and mixed-signal portions have not received systematic attention due to their complex nature and the fact that their optimization and simulation consume significant portions of the design cycle time. This paper presents a new approach to reduce the design cycle time by combining accurate polynomial metamodels and optimization algorithms. The approach relies on a mathematical representation (metamodel or surrogate model) of AMS-SoC subsystems/components. Polynomial metamodels are created from post-layout parasitic netlists and provide an accurate representation for each figure-of-merit over the entire design space of the AMS-SoC component. The metamodel approach saves a very significant amount of time during design iterations. Polynomial metamodels are reusable and language independent. Three algorithms are investigated to compare the speed for optimization on the polynomial metamodels. Two widely used circuits have been designed in two different technologies as comparative case studies: an 180 nm LC-VCO and a 45 nm ring oscillator (RO). Experimental results prove that the metamodel-based optimization achieved speed-up as high as 21,600 $\times$ for the LC-VCO circuit and 11,750 $\times$ for the RO in comparison to the actual circuit netlist-based (SPICE) optimization, with less than 1 % error. Thus, the paper demonstrates that the polynomial metamodeling approach to the design problem is an effective and accurate means for fast design space exploration and optimization.     

Immune Algorithm for Solving the Optimization Problems of Computer Communication Networks

The basic problem in optimizing communication networks is to assign a proper circuit for each origindestination pair in networks so as to minimize the average network delay, and the network optimal route selection model is a multi-constrained 0-1 nonlinear programming problem. In this paper, a new stochastic optimization algorithm, Immune Algorithm, is applied to solve the optimization problem in communication networks. And the backbone network vBNS is chosen to illustrate the technique of evaluating delay in a virtual network. At last, IA is compared with the optimization method in communication networks based on Genetic Algorithm, and the result shows that IA is better than GA in global optimum finding.     

An effective parameter extraction method based on memetic differential evolution algorithm

This paper provides an effective method for parameter extraction of microelectronic devices and elements. A novel method, memetic differential evolution (MDE) algorithm, is proposed in this paper. By combining differential evolution (DE) algorithm, mutations in immune algorithm (IA), and special operators for parameter extraction, MDE possesses characteristics of high accuracy, stability, generality, and efficiency. The effectiveness of the method has been shown by two typical examples, including small-signal equivalent circuit models for an AlGaN/GaN HEMT device up to 40 GHz, as well as an equivalent circuit model for on-chip differential spiral inductors. In both cases, the initial values and parameter ranges of the elements in the equivalent circuits are hard to determine in optimization. The results and comparisons with Levenberg-Marquardt (LM) algorithm, genetic algorithm (GA), particle swarm optimization (PSO) algorithm and canonical DE algorithm, demonstrate the superiority of MDE in terms of accuracy and generality.     

一种基于Kriging模型和受限差分进化的电磁结构快速优化算法

进化算法在各类电磁结构优化设计中有着广泛的应用，但由于需要在参数空间中进行随机搜索并仿真试探，优化效率普遍较低.针对这一问题，提出受限差分进化（Differential Evolution，DE）算法与Kriging代理模型相结合的电磁结构快速优化算法.算法根据参考设计结果建立圆柱管道空间，通过参数变换将进化区域限制在管道内部.Kriging模型学习管道内样本及其仿真数据，代替电磁仿真快速预测进化产生下一代种群的响应.相比整个参数空间，该算法DE寻优和Kriging学习的区域被显著减小，优化效率得到提升.通过一个波导双孔定向耦合器的优化设计，表明该方法的求解质量和收敛速度优于现有算法.     

螺旋天线的快速分析及宽带化设计

研究了一种快速计算螺旋天线电特性的新方法,并结合遗传算法对天线进行了宽带、小型化设计。采用直线段对法向模螺旋天线进行划分,应用合成基函数及合成检验函数的矩量法对阻抗矩阵进行了降阶处理,节省了求解矩阵方程所需的时间和存储量。结合遗传算法对加载法向模螺旋天线上的加载集总元件的值、加载位置以及匹配网络参数进行一体化优化设计,并采用Sherman-Morrison-Woodbury公式快速求解加载后的矩阵方程,提高了优化设计的效率。最后,成功设计了一副工作在100～1000MHz的宽带小型化螺旋天线。     

Inversion of ocean color observations using particle swarm optimization

Inversion of ocean color reflectance measurements can be cast as an optimization problem, where particular parameters of a forward model are optimized in order to make the forward-modeled spectral reflectance match the spectral reflectance of a given in situ sample. Here, a simulated ocean color dataset is used to test the capability of a recently introduced global optimization process, particle swarm optimization (PSO), in the retrieval of optical properties from ocean color. The performance of the PSO method was compared with the more common genetic algorithms (GA) in terms of model accuracy and computation time. The PSO method has been shown to outperform the GA in terms of model error. Of particular importance to ocean color remote sensing is the speed advantage that PSO affords over GA.     

Global Optimization of Wireless Seismic Sensor Network Based on the Kriging Model and Improved Particle Swarm Optimization Algorithm

This study established the Kriging model to simplify the mathematical model for calculations and to improve the operational efficiency of global optimization in seismic exploration engineering. Accordingly, wireless seismic sensor network (WSSN) was used as an example in this research, and the generated seismic data flow rate and the flow rate of seismic data transmission are the simulation sample points. Thereafter, the Kriging model was constructed and the function was fitted. An improved particle swarm optimization (PSO) was also utilized for the global optimization of the Kriging model of WSSN to determine the optimized network lifetime. Results show that the Kriging model and the improved PSO algorithm significantly enhanced the lift performance and computer operational efficiency of WSSN.