深亚微米HCI模型参数多目标全域提取方法

研究了一种建立在退化栅电流物理解析模型基础上的深亚微米pMOS器件HCI(hot carrier injection)退化模型.提出了一种基于L-M(Levenberg-Marquardt)算法的多目标响应全域优化提取策略,并对可靠性模型参数进行优化提取.分析了优化过程中由于参数灵敏度过低产生的问题并提出采用递归算法求解不同时刻栅电流注入电荷量的加速计算方法.最后,给出了最优化参数提取的结果,并且将测量值与理论值进行了比较,得到很好的一致性.     

深亚微米pMOS器件HCI退化建模与仿真方法

研究了深亚微米pMOS器件HCI(hot carrier injeotion)退化模型.采用流函数分析方法提出一种时变栅电流物理描述,基于这一栅电流模型改进了pMOS器件的HCI退化模型,并在该模型基础上提出一种HCI退化可靠性仿真方法,用于对静态应力条件下器件的HCI退化程度进行预测.最后给出了仿真结果对比.该方法已被用于XDRT可靠性工具中进行器件HCI退化分析.     

深亚微米pMOS器件HCI退化建模与仿真方法

研究了深亚微米pMOS器件HCI(hot carrier injeotion)退化模型.采用流函数分析方法提出一种时变栅电流物理描述,基于这一栅电流模型改进了pMOS器件的HCI退化模型,并在该模型基础上提出一种HCI退化可靠性仿真方法,用于对静态应力条件下器件的HCI退化程度进行预测.最后给出了仿真结果对比.该方法已被用于XDRT可靠性工具中进行器件HCI退化分析.     

一个新的pMOSFET栅电流退化模型

研究了最大栅电流应力(即pMOSFET最坏退化情况)下pMOSFET栅电流的退化特性.实验发现,在最大栅电流应力下,pMOSFET栅电流随应力时间会发生很大下降,而且在应力初期和应力末期栅电流的下降规律均会偏离公认的指数规律.给出了所有这些现象的详细物理解释,并在此基础上提出了一种新的用于pMOSFET寿命评估的栅电流退化模型.     

一个新的pMOSFET栅电流退化模型

研究了最大栅电流应力 (即 p MOSFET最坏退化情况 )下 p MOSFET栅电流的退化特性 .实验发现 ,在最大栅电流应力下 ,p MOSFET栅电流随应力时间会发生很大下降 ,而且在应力初期和应力末期栅电流的下降规律均会偏离公认的指数规律 .给出了所有这些现象的详细物理解释 ,并在此基础上提出了一种新的用于 p MOSFET寿命评估的栅电流退化模型     

通过直接栅电流测量研究PMOSFET's热载流子损伤

通过直接栅电流测量方法研究了热载流子退化和高栅压退火过程中PMOSFET's热载流子损伤的生长规律.由此,给出了热载流子引起PMOSFET's器件参数退化的准确物理解释.并证明了直接栅电流测量是一种很好的研究器件损伤生长和器件参数退化的实验方法.     

通过直接栅电流测量研究PMOSFET's热载流子损伤

通过直接栅电流测量方法研究了热载流子退化和高栅压退火过程中PMOSFET's热载流子损伤的生长规律.由此,给出了热载流子引起PMOSFET's器件参数退化的准确物理解释.并证明了直接栅电流测量是一种很好的研究器件损伤生长和器件参数退化的实验方法.     

GaAs MESFET肖特基结参数表征及其应用

运用双指数函数模型方法分析了影响GaAsMESFET肖特基势垒结特性的各种因素 ,编制了结参数提取和I -V曲线拟合软件 ,实现了通过栅源正向I-V实验数据提取反映肖特基势垒结特性的 6个结参数 ,其结果与实验数据吻合得很好。并对TiAl栅和TiPtAu栅GaAsMESFET进行了高温储存试验前后的结参数对比分析和深能级瞬态谱 (DLTS)验证分析 ,证明这种结参数表征方法是进行器件特性、参数的稳定性与退化和肖特基势垒结质量研究的一种新的实用可行的分析手段。     

基于分段拟合的SiC MOSFET模型优化方法

提出了一种基于分段拟合的SiC MOSFET模型优化方法.将表征静态特性的核心单元模型优化为分段模型,以使其能同时准确表征高、低栅压下器件的静态特性.同时,在模型分段处进行线性插值以保证模型函数的连续性,基于器件漏源支路元器件参数对拟合数据进行修正以提高模型表征参数的提取准确度.此外,为提高器件的动态模型准确度,增加了栅漏结电容模型函数的分段点,并给出了分段点选择方法.将优化前后栅漏结电容模型函数和静态仿真结果与测试获得的电容曲线、转移曲线和输出曲线进行对比,曲线匹配度较高,表明提出的优化方法显著提高了动态和静态模型的准确性.最后,搭建了SiC MOSFET开关特性测试平台,通过动态开关实验验证了模型优化方法的准确性.     

InP基HEMTs器件16参数小信号模型

针对InAlAs/InGaAs InP基 HEMTs提出了一种16参数小信号拓扑结构.拓扑结构中引入栅源电阻(Rgs)表征短栅沟间距引起的栅泄漏电流效应.另外还引入输出跨导(gds)和漏延迟(τds)描述漏端电压对沟道电流的影响以及漏源电容(Cds)引起的相位变化,从而提高了S22参数拟合精度.外围寄生参数通过open和short拓扑结构计算得出,本征部分利用去除外围寄生参数后的Y参数计算得出,最终模型参数值经过优化以达到最佳拟合状态而确定.结果表明,s参数和频率特性的仿真值与测试数据拟合程度很好,Rgs和τds的引入降低了模型误差.准确合适的InP基HEMTs小信号模型对于高频电路设计非常重要.     

PMOSFET's热载流子退化模拟及寿命评估的统一模型

对 PMOSFET's几种典型器件参数随应力时间的退化规律进行了深入研究 ,给出了一个新的器件退化监控量 ,并建立了不同器件参数退化的统一模型 .模拟结果和测量结果的比较表明 ,新的退化模型具有较高的准确性和较宽的适用范围 .新的退化模型不但可以用于器件参数退化量的模拟 ,也可以用于器件寿命评估     

An analytical model for hot carrier degradation in nanoscale CMOS suitable for the simulation of degradation in analog IC applications

Channel hot carrier (CHC) degradation is one of the major reliability concerns for nanoscale transistors. To simulate the impact of CHC on analog circuits, a unified analytical model able to cope with various design and process parameters is proposed. In addition, our model can handle initial degradation and varying stress conditions, allowing the designer to estimate the impact of CHC on transistor performance for arbitrary stressing patterns. The model is experimentally verified in a 65 nm CMOS technology. Expressions to simulate the impact of transistor degradation on relevant transistor parameters like output conductance and threshold voltage degradation are presented and verified.     

A Wiener process model for accelerated degradation analysis considering measurement errors

Accelerated degradation analysis plays an important role in assessing reliability and making maintenance schedule for highly reliable products with long lifetime. In practical engineering, degradation data, especially measured under accelerated condition, are often compounded and contaminated by measurement errors, which makes the analysis more challenging. Therefore, a Wiener process model simultaneously incorporating temporal variability, individual variation and measurement errors is proposed to analyze the accelerated degradation test (ADT). The explicit forms of the probability distribution function (PDF) and the cumulative distribution function (CDF) are derived based on the concept of first hitting time (FHT). Then, combining with the acceleration models, the maximum likelihood estimations (MLE) of the model parameters are obtained. Finally, a comprehensive simulation study involving two examples and a practical application are given to demonstrate the necessity and efficiency of the proposed model.     

基于40nm CMOS工艺电路的NBTI退化表征方法

负偏压温度不稳定性(NBTI)退化是制约纳米级集成电路性能及寿命的主导因素之一,基于40 nm CMOS工艺对NBTI模型、模型提参及可靠性仿真展开研究。首先对不同应力条件下PMOS晶体管NBTI退化特性进行测试、建模及模型参数提取,然后建立了基于NBTI效应的VerilogA等效受控电压源,并嵌入Spectre^TM仿真库中,并将此受控电压源引入反相器及环形振荡器模块电路中进行可靠性仿真分析,可有效反映NBTI退化对电路性能的影响。提出了一套完整可行的电路NBTI可靠性预测方法,包括NBTI模型、模型参数提取、VerilogA可靠性模型描述以及电路级可靠性仿真分析,可为纳米级高性能、高可靠性集成电路设计提供有效参考。     

A new prognostics method for state of health estimation of lithium-ion batteries based on a mixture of Gaussian process models and particle filter

State of health (SOH) estimation for batteries is a key component in the prognostics and health management (PHM) of battery driven systems. Due to the complicated operating conditions, it is necessary to implement the prognostics under uncertain situations. In this paper, a novel integrated approach based on a mixture of Gaussian process (MGP) model and particle filtering (PF) is presented for lithium-ion battery SOH estimation under uncertain conditions. Instead of directly assuming a certain state space model for capacity degradation, in this paper, the distribution of the degradation process is learnt from the inputs based on the available capacity monitoring data. To capture the time-varying degradation behavior, the proposed method fuses the training data from different battery conditions as the multiple inputs for the distribution learning using the MGP model. Then, a recursive updating of the distribution parameters is conducted. By exploiting the distribution information of the degradation model parameters, the PF can be implemented to predict the battery SOH. Experiments and comparison analysis are provided to demonstrate the efficiency of the proposed approach.     

Accelerated Discrete Degradation Models for Leakage Current of Ultra-Thin Gate Oxides

Using degradation measurements is becoming more important in reliability studies because fewer failures are observed during short experiment times. Most of the literature discusses continuous degradation processes such as Wiener, gamma, linear, and nonlinear random effect processes. However, some types of degradation processes do not occur in a continuous pattern. Discrete degradations have been found in many practical problems, such as leakage current of thin gate oxides in nano-technology, crack growth of metal fatigue, and fatigue damage of laminates used for industrial specimens. In this research, we establish a procedure based on a likelihood approach to assess the reliability using a discrete degradation model. A non-homogeneous Weibull compound Poisson model with accelerated stress variables is considered. We provide a general maximum likelihood approach for the estimates of model parameters, and derive the breakdown time distributions. A data set measuring the leakage current of nanometer scale gate oxides is analyzed by using the procedure. Goodness-of-fit tests are considered to check the proposed models for the amount of degradation increment, and the rate of event occurrence. The estimated reliabilities are calculated at lower stress of the accelerated variable, and the approximate confidence intervals of quantiles for breakdown time distribution are given to quantify the uncertainty of the estimates. Finally, a simulation study based on the gate oxide data is built for the discrete degradation model to explore the finite sample properties of the proposed procedure.     

Lifetime distribution based degradation analysis

The methods commonly used for degradation analysis deduce the lifetime distribution of a product from the distribution of the random parameters in the degradation path model for the product. This approach requires a functional form of the joint distribution of the random parameters, which poses certain practical difficulties. In this paper, we propose an alternative approach which makes inference directly on the lifetime distribution itself. In the proposed approach, degradation data are first used to derive predictive intervals of individual lifetimes. Then an imputation algorithm is invoked to obtain the estimate of the lifetime distribution. The approach has the following advantages: 1) the adequacy of the assumption on the lifetime distribution can be easily examined, 2) the estimated lifetime distribution has a closed form, and 3) the procedure can be more easily implemented. A simulation study is reported to demonstrate the efficiency of the proposed approach. The approach is applied to two real degradation data sets, and compared with Lu-Meeker's two stage method in the applications.     

Modeling and simulation of hot-carrier-induced device degradationin MOS circuits

The physical models and an integrated simulation tool are presented for estimating the hot-carrier-induced degradation of nMOS transistor characteristics and circuit performance. The proposed reliability simulation tool incorporates an accurate one-dimensional MOSFET model for representing the electrical behavior of locally damaged transistors. The hot-carrier-induced oxide damage can be specified by only a few parameters, avoiding extensive parameter extractions for the characterization of device damage. The physical degradation model includes both fundamental device degradation mechanisms, i.e., charge trapping and interface trap generation. A repetitive simulation scheme has been adopted to ensure accurate prediction of the circuit-level degradation process under dynamic operating conditions     

Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery

Electrical power system (EPS) is one of the most critical sub-systems of the spacecraft. Lithium-ion battery is the vital component is the EPS. Remaining useful life (RUL) prediction is an effective mean to evaluate the battery reliability. Autoregressive model (AR) and particle filter (PF) are two traditional approaches in battery prognosis. However, the parameters in a trained AR model cannot be updated which will cause the under-fitting in the long term prediction and further decrease the RUL prediction accuracy. On the other hand, the measurement function in the PF algorithm cannot be obtained in the long term prediction process. To address these two challenges, a hybrid method of IND-AR model and PF algorithm are proposed in this work. Compared with basic AR model, a nonlinear degradation factor and an iterative parameter updating method are utilized to improve the long term prediction performance. The capacity prediction results are applied as the measurement function for the PF algorithm. The nonlinear degradation factor can make the linear AR model suitable for nonlinear degradation estimation. And once the capacity is predicted, the state-space model in the PF is activated to obtain an optimized result. Optimized capacity prediction result of each cycle is utilized to re-train the regression model and update the parameters. The predictor keeps working iteratively until the capacity hit the failure threshold to calculate the RUL value. The uncertainty involved in the RUL prediction result is presented by PF algorithm as well. Experiments are conducted based on commercial lithium-ion batteries and real-applied satellite lithium-ion batteries. The results have high accuracy in capacity fade prediction and RUL prediction of the proposed method. The real applied lithium-ion battery can meet the requirement of spacecraft. All the experiments results show great potential of the proposed framework.