基于弹性网稀疏表示的芯片参数成品率估算方法

当前集成电路芯片参数成品率估算通常预设大量扰动基函数进行芯片性能模型构建,易造成成品率估算方法复杂度过高.而若随意减少扰动基函数数量,则极易造成成品率估算精度缺失.针对此问题,本文提出一种芯片参数成品率稀疏估算方法.该方法首先根据工艺参数扰动建立具有随机不确定性的漏电功耗模型;然后按照关键度高低,利用弹性网自适应选取关键扰动基函数对漏电功耗模型进行稀疏表示建模;最后,利用贝叶斯理论及马尔科夫链方法对漏电功耗成品率进行估算.实验结果表明,该方法不仅可以使所构建的漏电功耗模型具有一般性和稀疏性优点,而且能够对漏电功耗成品率进行准确估算,与蒙特卡罗仿真结果相比估算误差不超过5%.同时,相较于蒙特卡罗采样,该方法还可以大幅减少算法仿真时间,具有更好的仿真效率.     

基于Copula理论的芯片多元参数成品率估算方法

当前芯片参数成品率研究主要局限于单一性能指标成品率估算或对多个单性能指标成品率进行均衡优化.针对此类方法易造成参数成品率缺失的问题,本文提出一种基于Copula理论的芯片多元参数成品率估算方法.该方法首先针对漏电功耗及芯片时延性能指标,构建具有随机相关性的漏电功耗及芯片时延模型;然后利用鞍点线抽样方法对漏电功耗及芯片时延的边缘分布概率进行求解;最后根据Copula理论得到准确的芯片多元参数成品率估算结果.仿真结果表明,相较于蒙特卡罗仿真,本文方法具有较高的仿真效率,仿真时间减少了12%以上,而且在不同国际电路与系统研讨会（International Symposium on Circuits and Systems,ISCAS）基准电路下,该方法与蒙特卡罗仿真结果的相对误差均保持在9%以内,能够在任意性能指标约束下,对芯片多元参数成品率进行有效估算,可为芯片设计人员提供同时考虑多个性能指标的参数成品率信息.     

IC优化设计的参数抽样效益折衷算法

本文对效益函数进行了扩展,提出了IC最优化设计的参数抽样效益折衷方法,较好地解决了IC统计多目标优化非劣解的协调问题,得到了一种改进的Monte Carlo成品率最优化设计方法.     

基于粒子群算法的钻进参数多目标优化

为了使钻进过程达到最优,提出了基于机械钻速、钻头寿命和钻头比能的钻进参数多目标优化模型。参考典型的多目标优化进化算法NSGA-Ⅱ,提出了一种多目标粒子群算法(MOPSO)。采用一个钻进参数优化实例对优化模型和算法进行检验,得到分布均匀的Pareto最优解,一些最优解与传统的钻进参数单目标优化的解近似;讨论了算法中的种群规模、迭代次数和外部档案规模三个参数,得到一组兼顾解质量和计算时间的参数值,其计算时间的统计结果证明模型和算法满足钻进参数动态优化的要求。     

55nm工艺下SoC漏电功耗优化方法研究

随着工艺节点快速演进到深亚微米,芯片设计的复杂度大幅增加,高性能低功耗的构架逐渐成为主流设计要求.尤其是工艺发展到65nm及以下时,漏电功耗开始极速增大,在高性能要求不变的同时,要兼顾低功耗需求,这对芯片设计人员是个巨大的挑战.以55nm工艺的SoC设计为例,通过多阈值电压优化漏电功耗的方法,在芯片物理设计阶段,对设计的漏电功耗进行优化,使得设计性能和功耗满足需求.     

解卷积主动声呐目标回波高分辨时延估计技术

为提高浅海复杂海洋环境下的目标回波时延估计精度,增强主动声呐系统对目标的探测能力,该文基于稀疏表示理论和解卷积思想,提出一种高分辨目标回波时延估计技术。首先,引入Toeplitz算子,将发射信号的不同时延结果构造成时延字典矩阵,时延估计值存在于所求解的稀疏向量中。其次,利用交替方向乘子算法(ADMM)优化框架,求解全局最优解。最后,采用一种加权迭代策略设置正则化参数,进一步抑制多途信道的影响,解耦合出回波到达的时刻,获得高精度的目标回波时延估计结果。数值仿真和实验数据表明,该文提出的解卷积主动声呐目标回波高分辨时延估计技术可以在多途扩展严重的浅海声信道实现目标探测,在信道水池的实验环境下,时延估计分辨率可达0.056 ms。     

解卷积主动声呐目标回波高分辨时延估计技术

为提高浅海复杂海洋环境下的目标回波时延估计精度,增强主动声呐系统对目标的探测能力,该文基于稀疏表示理论和解卷积思想,提出一种高分辨目标回波时延估计技术.首先,引入Toeplitz算子,将发射信号的不同时延结果构造成时延字典矩阵,时延估计值存在于所求解的稀疏向量中.其次,利用交替方向乘子算法(ADMM)优化框架,求解全局最优解.最后,采用一种加权迭代策略设置正则化参数,进一步抑制多途信道的影响,解耦合出回波到达的时刻,获得高精度的目标回波时延估计结果.数值仿真和实验数据表明,该文提出的解卷积主动声呐目标回波高分辨时延估计技术可以在多途扩展严重的浅海声信道实现目标探测,在信道水池的实验环境下,时延估计分辨率可达0.056 ms.     

VLSI集成电路参数成品率及优化研究进展

VLSI的参数成品率是与制造成本和电路特性紧密相关的一个重要因素,随着集成电路(IC)进入超深亚微米发展阶段,芯片工作速度不断增加,集成度和复杂度提高,而工艺容差减小的速度跟不上这种变化,因此参数成品率的研究越来越重要.本文系统地讨论了参数成品率的模型和设计技术研究进展,分析不同技术的特点和局限性.最后提出了超深亚微米(VDSM)阶段参数成品率设计和成品率增强面临的主要问题及发展方向.     

基于TAM分组策略的SoC测试多目标优化设计

在片上系统芯片（System-on-Chip ,SoC）测试优化技术的研究中,测试时间和测试功耗是相互影响相互制约的两个因素。在基于测试访问机制（Test Access Mechanism ,TAM ）分组策略的基础上,以测试时间和测试功耗为目标建立了联合优化模型,运用多目标遗传算法对模型进行求解。以ITC’02标准电路中的p93791电路为实例进行验证,表明此方法能够在测试时间和测试功耗的优化上获得较理想的解,且能提高TAM通道的利用率。     

正交压缩采样雷达偏离网格目标时延估计技术

参数扰动正交匹配追踪是一种有效的偏离网格目标时延估计技术.但是,该方法在每次迭代时只搜索一个目标,计算量大.本文提出一类低计算量的偏离网格目标时延估计技术——参数扰动带排除贪婪重构算法.该算法在贪婪重构方法中引入带排除技术,用于检测多个与目标最邻近的离散网格,利用参数扰动技术来估计目标与最邻近离散网格之间的时延偏差.本文以正交压缩采样雷达为例,采用回溯自适应正交匹配追踪方法,研究参数扰动带排除贪婪重构算法性能.仿真实验表明,与已有的相关方法相比,该算法在不影响估计精度的情况下可减少一倍以上的计算时间.     

Energy-Spectral-Efficiency Tradeoff in Interference-Limited Wireless Networks

Spectral efficiency (SE) is an important metric in traditional wireless network design. However, as the development of high-data rate services and rapid increase of energy consumption, energy efficiency (EE) has received more and more attention. In this paper, we investigate the EE–SE tradeoff problem in interference-limited wireless networks. Different from previous researches, we try to optimize EE and SE simultaneously. Firstly, the problem is formulated as a multi-objective optimization problem (MOP), with the constraint of transmit power limit. Then, we convert the MOP to a single-objective optimization problem by the weighted linear sum method. We present an algorithm utilizing difference between two convex functions programming (DCP) to handle with SE optimization problem (SD). EE optimization problem can be solved by an algorithm (EFD) consists of fractional programming embedded with DCP. While for EE–SE tradeoff problem, a particle swarm optimization algorithm is proposed (ESTP) to deal with it. Simulation results validate that the proposed algorithm can efficiently balance EE and SE by adjusting the value of weighted coefficient, which could be used to design a flexible energy efficient network in the future.

     

Optimized magnetic field control of an electromagnetic actuation system for enhanced microrobot manipulation

This paper presents a novel magnetic field control method to enhance the performance in terms of accuracy and field strength of underdetermined electromagnetic actuation systems. Conventional control algorithms prioritize low overall power usage, and they fail to cope with the saturation of electromagnets due to a limited power supply, which limits the magnetic and gradient field strength of a system. In this study, we prioritize magnetic and gradient fields, among other priorities, by developing computational optimization programming to find alternative solutions that satisfy constraints. Single- and multi-objective optimization techniques are developed and compared to address the problem. The latter algorithm, which employs the weighted sum method to obtain Pareto-optimal solutions, demonstrates better performance than the former one. The proposed control methodology is characterized and validated by simulation and experiments. The maximum magnetic and gradient fields were significantly enhanced by the proposed algorithm; by approximately 35% and 70%, respectively, compared to the conventional independent control applied for our system and by 24% and 64%, respectively, for a benchmark system.     

Weighted sum rate maximization for OFDM-based cognitive radio systems

In this paper, we focus on the subcarrier and power allocation problem in the downlink of an OFDM system under the cognitive radio environment. We aim to maximize the weighted sum rate of secondary users, without causing adverse interferences to primary users. We formulate the optimization problem subject to a total transmit power constraint and interference constraints, and give the optimality conditions, from which we derive a power limited multilevel water-filling algorithm. Simulation results show that our proposed algorithm yields significant improvement in terms of weighted sum rate, and provides good convergence with low computational burden.     

多目标优化问题的遗传算法改进研究

基于Nash均衡的思想在NSGA所求得的Pareto最优解基础上,探讨一种能对多目标优化问题进行求解的遗传算法。采用Nash均衡的思想在多目标优化的遗传算法,结合NSGA算法,提出一种能得到多个Pareto最优解的多目标优化算法。通过目标函数线性加权法、NSGA对函数进行了试验分析,对部分自变量进行固定,对其他的自变量进行优化,对Pareto最优解进行持续优化,进而实现加速算法的收敛,从实验中得出了这种算法具有较快的收敛性,但是其运行时间和NSGA相比没有多少改善。     

Joint backhaul bandwidth and power allocation in heterogeneous cellular networks: A two‐level approach

We investigate the problem of joint downlink wireless backhaul bandwidth (WBB) and power allocation in heterogeneous cellular networks (HCNs). A WBB partitioning scheme is considered, which allocates the whole bandwidth between the macrocell and small cells for data transmission and backhauling. We formulate an optimization problem to maximize the weighted sum logarithmic utility function by jointly optimizing WBB portion and fronthaul power allocation of each base station with consideration of the backhaul capacity limitation on each small cell. In order to solve this joint optimization problem, we propose a hierarchical two‐level approach and decompose the original problem into two independent subproblems: the WBB allocation at the macrocell base station (MBS) and the power allocation at both the MBS and small cell base stations (SBSs). Accordingly, the optimal WBB portion and power allocation solutions are obtained, respectively. Furthermore, we develop a distributed algorithm to implement the joint WBB and power allocation. Numerical results verify the effectiveness of the proposed approach and analyze the impact of the weighted coefficient and backhaul capacity limitation on the network performance. In addition, significant performance gains can be achieved by the proposed approach over the benchmark.     

Low power PLL with reduced reference spur realized with glitch-free linear PFD and current splitting CP

The integrated circuit industry not only demands finding optimal design solutions over an efficient process, but also requires the address of increased reliability challenges, caused by strong electric fields, to elevate the overall system robustness. This work reconciles such an inconsistency by proposing a transistor-level methodology for current steering DACs to map their specs onto transistor sizes and improve their performance, parametric yield as well as lifetime reliability through the state of the art evolutionary tools and reliability simulator. The tradeoff between performance specs and power consumption is obtained through a multi-objective approach by considering process variations together with lifetime stress effects. The 10-bit DAC@130MS/s is herein implemented using TSMC CMOS 0.18 μm technology. Demonstrated results point out optimal design points in short and long time reliability as well as performance parameters over a computational effective procedure.     

多小区下行链路系统的资源效率最大化协同波束成形算法

本文研究了基于资源效率优化的多小区多用户协同波束成形算法设计。为了权衡系统频谱效率与能源效率,考虑在单基站发射功率约束以及达到用户服务质量需求条件下最大化系统资源效率,即系统能源效率与归一化系统频谱效率的加权和。由于优化变量之间的耦合性以及约束条件的非凸特性,该优化问题是一种非凸优化问题并且难以直接获得最优解。为了求解所考虑的优化问题,本文联合利用凸近似方法和分数规化理论,提出一种多小区下行链路系统中最大化资源效率的交替优化算法。所提算法的收敛性可以由凸近似方法和单调有界理论保证。同时,数值仿真验证了所提算法的有效性。      

基于约束多目标免疫算法的多载波卫星通信系统链路支持性优化

毫米波通信系统因为带宽宽和易于小型化的特点被广泛应用于卫星通信中,基于提高多载波卫星毫米波通信系统容量的链路支持性优化面临诸多性能指标权衡的难题,如:降低上行EIRP需求,提高转发器功率利用率,增强链路稳健性等,将多目标优化思想引入到毫米波卫星通信系统的支持性优化模型,提出一种约束多目标免疫算法(Constrained Nondominated Neighbor Immune Algorithm,C-NNIA),并进行了相应的建模、仿真和优化.表明该算法可获得优于传统直接求解(Direct Search,DS)算法和基于单目标可变邻域搜索(Variable Neighbourhood Search,VNS)算法的Pareto最优解,且算法具有更好的约束处理能力、多样性保持能力以及快速收敛特性.最后,通过对最优解集的设计变量以及性能度量参数的统计和分析,获得了具有指导性的设计参考.