Manufacturing performance evaluation for IC products

As we known, the product diversity and complexity in the production line will gradually increase. When the multiple products were alternately produced at the same line, the manufacturing performance will be difficult to evaluate. In particular, traditional process capability analysis and related process capability indexes cannot be directly employed to the IC manufacturing process. As we know, the yield has a direct effect on the manufacturing cost. Hence, yield is frequently used by most IC manufacturers to evaluate manufacturing performance. The diversity of function will become another analytic consideration due to that the component density, wafer area and product complexity of an IC product rapidly increase. Hence, the diversity of function can be regarded as the evaluated factor. Additionally, the defects on a wafer will begin to cluster as the wafer area gradually increases. Therefore, only using the yield to represent manufacturing performance may not lead to an appropriate judgment. In particular, only using the yield to evaluate the process’s stability and the product’s maturity can not provide a meaningful resolution. The primary reason is that the inherent features in the processes or products are not included into analyzing. For instance, even though the defect count, defect size and defect distribution are the same, the yield loss of the complicate manufactured product will be less than that of the simple manufactured product. In this study, we propose a simple performance evaluation index to assess the manufacturing performance in the IC manufacturing industry. This evaluation index is constructed according to a modified Poisson yield model, and the related parameters regarding the process or product (e.g., the minimum linewidth, the area of a die, the number of manufactured process or layer, the degree of defect clustering, and so on.) are taken into consideration. In addition, an integrated evaluation procedure is also suggested to evaluate the performance of the manufacturing of multiple IC products. According to the result obtained from the illustrative example, the index and the procedure can overcome the drawback of separately using yield or defect count in the analysis. The rationality and the feasibility of the proposed evaluated index and the procedure can be verified by demonstrating the illustrative example.     

Bayesian spatial defect pattern recognition in semiconductor fabrication using support vector clustering

Defects generated during integrated circuit (IC) fabrication processes are classified into global defects and local defects according to their generation causes. Spatial patterns of locally clustered defects are likely to contain the information related to their defect generation mechanisms. In this paper, we propose a model-based clustering for spatial patterns of local defects to reflect real situations. A flexible two-step approach is proposed to classify the spatial defects patterns via support vector clustering and Bayesian method. Support vector clustering is employed to separate global defects from the local ones to improve both clustering accuracy and computational efficiency in further analysis. A new mixture model is proposed for modeling the distribution of local defects on the wafers. Local defect clusters with amorphous/linear, curvilinear, and ring patterns are modeled by multivariate normal distribution, principal curve, and spherical shell, respectively. A Bayesian inference procedure is then applied for parametric pattern recognition of the local defects. Results from both simulated data and real wafer map data demonstrate potential in applying our approach to analyze general defect patterns in IC manufacturing.     

[质量优化]数据驱动的晶圆图缺陷模式识别方法

针对晶圆生产过程中晶圆图数据角度与维度多样性和数量不平衡的特点,提出了基于对抗生成网络的晶圆图缺陷模式识别方法。设计了Radon变换,实现了数据多角度的统一;采用重采样机制实现数据多维度的缩放,得到了标准晶圆缺陷数据。提出了基于对抗生成网络的晶圆缺陷分类方法,利用生成机制平衡各缺陷模式的样本数量,以提升缺陷模式识别精度。试验结果表明,该方法可大幅提升少类样本的识别精度,且在整体识别率上远优于支持向量机和Adaboost算法。     

An effective defect identification scheme in pipeline ultrasonic testing

The identification of a defect from its pulse-echo ultrasonic signal is a difficult task but greatly significant for defect evaluation in pipeline ultrasonic testing. In this work, an effective defect identification scheme, which uses the deconvolution technique, is developed to identify the ultrasonic signals from simulated defects with two different shapes (a notch and a transverse cylindrical cavity) on a pipeline specimen. A DSF (Defect Similarity Function) defined as a deconvolution of a target signal by a reference signal is used to classify the defects. Experimental results indicate that the DSFs for the same type of defect-reference signals are narrow impulselike patterns with larger amplitudes, while those for the different types of defect-reference signals are broad impulselike patterns with relatively smaller amplitudes. Furthermore, the DSF is sensitive to defect shapes but non-sensitive to the measuring distance and defect size. Therefore, the defects can be identified from the pattern of the DSF. The proposed technique shows great potential for the identification of defects via ultrasonic signals in the pipeline inspection. This text was submitted by the authors in English.     

A New Statistical Model of Contact Fatigue

A detailed derivation of a new statistical model of contact fatigue life followed by its qualitative and quantitative analysis are presented. The model is based on contact and fracture mechanics and statistical treatment of the initial distribution of material defect. The model assumptions and their validation as well as the model properties are discussed. A parametric study of the model is performed. A generalization of the model for the case of stochastic residual stress or other contact parameters is proposed. Some analytical formulas for calculation of contact fatigue are proposed and analyzed. The validation of the model and its applicability to calculation of bearing fatigue life and some particular data are considered. A reflection of the quality of bearing manufacturing process on the contact fatigue model is discussed.     

结合暗场散射与曲率成像的镜面缺陷检测方法

在镜面表面缺陷检测技术中,常用的暗场成像检测法对于波纹类的三维镜面缺陷难以形成对比度,为此提出了一种结合暗场散射法和曲率成像法的综合型镜面表面缺陷检测方法.分别对暗场成像原理和基于条纹反射的曲率成像原理进行了理论分析;构建了结合两种成像模块的综合型镜面缺陷检测的实验装置;对注塑反射镜和抛光玻璃薄片两种样品进行了缺陷成像...     

Detecting surface and volume defects in metals by the laser-acoustic method

An all-purpose non-destructive method for detecting surface and volume defects in metallic objects has been developed. This technique is based on scanning the surface of a studied object with a focused laser beam and detecting the generated acoustic waves with a piezoelectric sensor. Defect control is performed by measuring the amplitude and propagation time of an acoustic signal to a detector, when the laser beam passes a defect zone.     

基于有限元方法的钢板焊缝缺陷红外无损检测

应用有限元方法于红外无损检测中，利用大型有限元分析软件ANSYS，构建不同尺寸、深度的缺陷，施加单面局部热流，计算被测物体表面的温度分布，借助图像处理得到表面温度分布云图，根据热像特征和表面温升，从定性和定量上得到对缺陷更为可信的判定。工作重点在于研究缺陷尺寸、位置与表面温度间的关系，旨在使红外无损检测从定性检测向定量检测接近，为制定焊缝缺陷准确可靠的检测标准提供依据。     

考虑二元Copula统计量的晶圆制造叠加误差监测

目前,大多数晶圆制造研究集中在基于离散数据的缺陷模式识别上,而芯片的光刻制造是连续叠加过程,因此基于连续数据的晶片重叠误差监测具有挑战性和必要性。在数据监测过程中充分考虑数据的可解释性,同时结合晶圆数据特性及其物理意义加入新的惩罚项,改进LTS-SPCA降维模型,提出了灵活度较高的稳健稀疏主成分分析技术;然后基于Copula的置换对称、反射对称两种性质,考虑晶圆的几何特征,建立了最佳多元耦合统计量,用于监测晶圆制造的叠加过程异常。所提方法监测异常数据的准确率可达91.75%,具有较高的工程应用价值。     

小径薄壁管座角焊缝典型缺陷的超声相控阵CIVA仿真研究

在电厂安放式小径薄壁管座角焊缝超声检测技术中,检测结果易受到管径小、检测位置结构复杂等因素影响,因此,采用CIVA软件对焊缝模型进行超声相控阵声场仿真,得到未熔合、未焊透、裂纹和夹渣4种典型缺陷在不同检测位置曲率下的缺陷响应.对比分析仿真缺陷图谱,研究检测位置曲率对缺陷成像和声压幅值的影响,结合优化后的仿真参数和检测方...     

混合聚类新算法及其在故障诊断中的应用

针对模糊C-均值(FCM)聚类算法假设各维特征和每个样本对聚类贡献相同,同时需要预先设定聚类数的不足,利用3层前馈神经网络、点密度函数算法和聚类有效性指标对其进行改进,提出一种新的混合聚类算法。该算法考虑到不同特征和不同样本对聚类结果有不同程度的影响,并根据聚类有效性指标的变化自适应确定聚类数来实现聚类。利用基于梯度下降的3层前馈神经网络通过无监督训练来自适应学习特征权值,使用基于点密度函数的算法获取样本权值,给不同特征和不同样本赋予权重,突出敏感特征和典型样本的主导作用,抑制其他特征和样本对聚类的干扰,以提高聚类性能。研究结果表明,对于国际标准测试数据和某机车轴承的早期故障诊断,该混合聚类算法不但能自动确定聚类数,而且聚类的准确性明显比FCM高。     

Desulfurization process using Takagi–Sugeno–Kang fuzzy modeling

Two Takagi–Sugeno–Kang fuzzy models for the prediction of the amount of reagents for desulfurization in steel processing are developed from experimental data. For the design of the models, an algorithm was proposed to be used in the procedures of the two phases: structure building and parametric identification. In the first phase, the Gustafson–Kessel clustering algorithm with the cluster validity index was proposed to find the number of fuzzy rules and an initial fuzzy model. In the second phase, a gradient-descent-based approach was developed and used for optimized tuning of membership functions of the fuzzy model. The numerical results were compared with a conventional statistical model and neural networks and adaptive network-based fuzzy inference system.     

小波分析在裂纹型缺陷超声无损定量检测中的应用研究

根据超声检测的基本原理,建立了缺陷深度定量识别模型,结合超声信号自身的特点,应用具有多分辨率分析能力的小波方法对超声信号进行处理,提出了超声信号小波分析基函数的选择以及分解最大尺度的确定方法。对超声信号进行小波压缩,发现处理后的信号比原始波形更能体现出缺陷波的时域特征,提出了缺陷小波特征峰的概念,根据该特征峰所对应的时域位置建立了缺陷深度的物理测算方法。     

Eccentricity estimation with error modeling in dynamic wafer handling

Wafer-handling robots are used to transfer wafers in semiconductor manufacturing. Typically, a pick–measure–place method is used to transfer wafers accurately between stations. The measurement step is performed using an aligner, which is time-consuming. To increase the wafer transfer efficiency, it is desirable to speed up the measurement process or place it in parallel with other operations. Hence, optic sensors are installed at each station to estimate the wafer eccentricity on the fly. The eccentricity values are then used to control the robot to place the wafer directly onto another station accurately without using the aligner. In this paper, the kinematic model of a wafer-handling robot is developed. The errors in the kinematics model are analyzed. A model parameter identification method is proposed to obtain the parameters. A wafer eccentricity identification method is derived to calculate the eccentricity values on the fly. Experiments were performed to validate the proposed methods. The computed eccentricity errors are compared with those obtained by other researchers. The results demonstrated that the kinematics error modeling method can increase the wafer eccentricity identification accuracy. Hence, the developed methods can be applied to estimate the wafer eccentricity on the fly to reduce the wafer transfer cycle time and increase wafer-handling efficiency.     

利用图像处理技术评价硅片表面清洗率

介绍了一种基于Matlab图像处理工具箱技术的评价硅片表面污染颗粒激光清洗率的新方法。借助Matlab图像处理工具箱,对清洗前后硅片表面光学显微镜照片进行处理,编写硅片表面激光干法清洗率的评价程序,统计清洗前后硅片表面评价区域的污染颗粒个数,对清洗效果进行定量评价。研究结果证明,利用此方法统计的颗粒数准确度达97.6%,得到的激光清洗率准确度达99.2%。结果表明,借助图像处理技术评定清洗效果是一种高效、快速、准确的新方法。     

内置皮拉尼计的硅通孔圆片级MEMS真空封装研究

针对现有的圆片级真空封装存在检测难、易泄漏等问题,提出了内置皮拉尼计的硅通孔圆片级MEMS真空封装方法。研制了用于圆片级真空封装导线互连的硅通孔,探讨了玻璃盖板与硅圆片之间阳极键合工艺与硅圆片与硅圆片之间的金硅共晶键合工艺,研制了用于检测封装壳体内部真空度的皮拉尼计; 研制了内置皮拉尼计的4英寸硅通孔圆片级真空封装,研制了低温激活非蒸散型吸气剂。实验研究表明,该研究解决了长时间保持真空度的问题。     

多功能真空键合设备的研制

基于圆片键合技术，设计了一种在真空条件下进行圆片键合的工艺设备。在该设备的基础上，进行了阳极键合与金硅键合2种典型的键合试验，键合出来的样品结合强度高而且没有缺陷，验证了该设备的实用性。     

有限元方法在单面热流加热红外无损检测中的应用

利用大型有限元分析软件ANSYS,构建不同形状、尺寸的缺陷,施加单面热流,计算被测物体表面的温度分布,借助图像处理得到表面温度分布云图,根据热像特征和表面温升,从定性和定量上得到对缺陷更为可信的判定。工作重点在于研究缺陷尺寸、形状与表面温度间的关系,旨在使红外无损检测从定性检测向定量检测接近,为制定准确可靠的检测标准提供依据。研究表明有限元方法应用于红外无损检测是行之有效的。这对于红外无损检测具有一定的工程应用价值和参考意义。     

一种基于非晶层粘性流动的机械化学抛光模型

通过分析单个微纳米磨粒滑动接触的分子动力学模拟的研究结果，提出了在典型的机械化学抛光（CMP）过程中芯片表面材料的去除应为表面非晶层物质粘性流动所致的新观点。基于这种机理，应用微观接触力学和磨粒粒度分布理论建立了一种新的表征CMP过程材料去除速率的数学模型。模型中引入了一个表征单个磨粒去除芯片表面非晶层能力的比例系数k，k综合反映了磨粒的机械作用、抛光液对芯片表面的化学作用和芯片的材料特性。通过实验验证发现该模型的理论预测值与实验测定值十分吻合。     

Experimental verification of super-resolution optical inspection for semiconductor defect by using standing wave illumination shift

Semiconductor design rules and process windows continue to shrink, so we face many challenges in developing new processes such as the less 100-nm design rule and the 300-mm wafer. The challenges have become more difficult, and the next generation of defect inspections is urgently needed. Optics and electron beams have been primarily used for the detection of critical defects, but both technologies have disadvantages. The optical inspection is generally not sensitive enough for defects at 100 nm or less, while the scanning electron microscopy inspection has low throughput because it takes a long time to scan 300 mm. To find a solution to these problems, we have proposed a novel optical inspection method for the critical defects on the semiconductor wafer. First, we carried out theoretical examinations and computer simulations. As a result, the proposed method makes it possible to observe a structure with robustness and higher resolution beyond the Rayleigh limit. Second, we developed an apparatus for the basic experiment and carried out the super-resolution experiment using a line and space sample. As a result, the sample structures were clearly resolved beyond the Rayleigh limit and defects on the sample surface were detected with super-resolution.