应用于三维集成电路解析式布局的层分配算法

层分配是解析式三维集成电路布局算法中的关键一步。解析式布局需要通过层分配将连续的三维空间中的单元划分到二维的芯片层上,这个过程会破坏之前三维空间中得到的连续解。为了实现从优化的三维布局到合法的多层二维结构的平滑过渡,提出一种使用最小代价流的层分配方法,尽可能地继承三维优化结果,保护解空间。将此层分配算法嵌入到多层次的解析式三维集成电路布局算法中,以总线长和穿透硅通孔数目的加权总和为目标,面积密度为约束条件,对比当前其他三维布局算法,该算法得到较好的线长结果、穿透硅通孔数量和运行时间。     

一种用于VLSI的统一通孔最少化和线长最小化层分配算法

本文提出了统一通孔最少化和线长最小化层分配的图论模型．它考虑到不同层电性能不等对线长分布的特殊要求，给出了指定层上线长最小化的形式化描述．在此基础上，本文给出了通孔最少化和线长最小化的统一组合目标，并用一种启发式算法去求解层分配算法．文中还讨论了工程中的许多实际问题的处理方法．算法已用Ｃ语言在ＳＵＮ工作站上实现．实验结果表明，算法十分有效且稳定．     

Evaluation of Si liquid cooling structure with microchannel and TSV for 3D application

The development of 3D integration has caused a major technology paradigm shift to all integrated circuit (IC) devices, interconnects, and packages. Despite the benefits of 3D integration, this process faces the key challenge of thermal management, especially for high power and high density IC devices. Due to the limitations of conventional thermal solutions, liquid cooling technology has become a field of great interest for IC thermal management. In this study, an on-chip liquid cooling module with three different through Si vias (TSVs) and a fixed microchannel structure has been fabricated on an Si wafer using deep reactive ion etching and anodic bonding, followed by a grinding and dicing process. Pressure drop, coolant flow, and temperature difference before and after liquid flow were experimentally measured. TSV depth and diameter have been shown to have little effect on the change of pressure drop; however, shallower TSV depth and larger TSV diameter led to improved liquid cooling performance. The trapezoidal TSV showed slightly more effective cooling than did the scalloped TSV or the straight TSV.

     

一种用于硅通孔的阻挡层和种子层一体化制备工艺研究

介绍了一种硅通孔中阻挡层和种子层制备的新型工艺方法。利用磁控溅射的方法在SiO2上沉积1μm的Ti膜。表层的Ti膜湿法氧化后作为种子层,底层Ti膜作为阻挡层。填充材料选择Cu,利用电镀的方法填充。利用热退火的方法来测试Cu的扩散性能,即Ti膜的阻挡特性,热退火试验选择350,400,450℃三种不同的温度。XPS结果表明:在400℃及以下温度阻挡层成功阻挡了Cu的扩散。目前已在硅通孔中初步实现此种结构并完成通孔中Cu的填充。     

The approximation between thermal sensation votes (TSV) and predicted mean vote (PMV): A comparative analysis

The Fanger's predicted mean vote (PMV) model is used to evaluate thermal comfort. However, when PMV is compared to people's real thermal sensations, collected in field studies, some discrepancies are verified. One of the components for the calculation of PMV is clothing surface temperature (t_cl), which can be a factor that contributes towards these discrepancies. The aim of this study was to propose alternative methods for predicted mean vote, seeking to reduce these discrepancies. The mathematical Newton's method was applied to obtaining t_cl values. The PMV₁ was determined by replacing the t_cl values in the traditional equation of PMV as described by ISO 7730 (2005). The second model of thermal prediction, named as PMV₂, was obtained by a multiple linear regression considering the thermal sensation votes, the metabolic rate and the six heat exchange mechanisms. Two groups (welders and army officers) were used to verify the accuracy of the methods used in this research. The results show that both methods were able to describe the thermal sensation votes. For the welder group, both PMV₁ and PMV₂ overestimated the results: when people voted TSV = 0, PMV₁ = 0.64 and PMV₂ = 0.23. In the case of the army officers group, applying PMV₁, when TSV = 0, PMV₁ = 1.47. The application of the multiple regression increased the potential of PMV₂ to obtain responses closer to those provided by the occupants of the thermal environment studied: when TSV = 0, PMV₂ = 0.0068, demonstrating a greater effectiveness of this method.     

The ensemble approach to neural-network learning and generalization

A method is suggested for learning and generalization with a general one-hidden layer feedforward neural network. This scheme encompasses the use of a linear combination of heterogeneous nodes having randomly prescribed parameter values. The learning of the parameters is realized through adaptive stochastic optimization using a generalization data set. The learning of the linear coefficients in the linear combination of nodes is achieved with a linear regression method using data from the training set. One node is learned at a time. The method allows for choosing the proper number of net nodes, and is computationally efficient. The method was tested on mathematical examples and real problems from materials science and technology.     

Study on TSV isolation liners for a Via Last approach with the use in 3D-WLP for MEMS

This paper discusses approaches for the isolation of deep high aspect ratio through silicon vias (TSV) with respect to a Via Last approach for micro-electro-mechanical systems (MEMS). Selected TSV samples have depths in the range of 170…270 µm and a diameter of 50 µm. The investigations comprise the deposition of different layer stacks by means of subatmospheric and plasma enhanced chemical vapour deposition (PECVD) of tetraethyl orthosilicate; Si(OC₂H₅)₄ (TEOS). Moreover, an etch-back approach and the selective deposition on SiN were also included in the investigations. With respect to the Via Last approach, the contact opening at the TSV bottom by means of a specific spacer-etching method have been addressed within this paper. Step coverage values of up to 74 % were achieved for the best of those approaches. As an alternative to the SiO₂-isolation liners a polymer coating based on the CVD of Parylene F was investigated, which yields even higher step coverage in the range of 80 % at the lower TSV sidewall for a surface film thickness of about 1000 nm. Leakage current measurements were performed and values below 0.1 nA/cm² at 10 kV/cm were determined for the Parylene F films which represents a promising result for the aspired application to Via Last MEMS-TSV.     

Least squares pole assignment by memoryless output feedback

In this paper, a pole assignment problem for a linear time-invariant control system by memoryless output feedback is posed as a least squares optimisation problem and analysed. The cost functions are appropriately modified so as to obtain convergence of the corresponding gradient flow and existence of the global minimum. This approach is also extended to accommodate the output pole assignment problem with insensitivity against disturbances in system parameters. The relation between the modified cost functions and the original pole assignment problem is revealed. The proposed approach is compared with other existing approaches and illustrated by numerical results.     

Machine learning method for CPTu based 3D stratification of New Zealand geotechnical database sites

Three-dimensional (3D) geotechnical site stratification is of vital importance in geotechnical practice. In this study, a set of methods for 3D site stratification based on CPTu measurements of New Zealand Geotechnical Database (NZGD) sites is proposed. One-dimensional (1D) soil stratification at discrete CPTu points is first conducted and then interpolated in 3D to achieve 3D site stratification. 1D soil stratification is achieved through a proposed soil classification model combined with a proposed soil layer boundary identification method, which achieves a correct soil profile length identification rate of 93%. The soil classification machine learning model classifies the soil within NZGD into three types, i.e. Gravel, Sand, and Silt, and is able to reflect the fines content for silty sand. The model innovatively incorporates local variation information of CPTu curves in the input for a random forest algorithm to significantly improve identification accuracy to over 90%. Accurately locating soil layer boundaries is achieved through proposing a modified WTMM boundary identification method. 3D site stratification is then realized through 3D interpolation of 1D stratification at discrete CPTu points using a generalized regression neural network (GRNN) method. The 3D site stratification method is validated for two independent geotechnical sites within NZGD, exhibiting the effectiveness of the proposed set of methods.     

PMV model is insufficient to capture subjective thermal response from Indians

A controlled laboratory experiment was carried out on forty Indian male college students for evaluating the effect of indoor thermal environment on occupants' response and thermal comfort. During experiment, indoor temperature varied from 21 °C to 33 °C, and the variables like relative humidity, airflow, air temperature and radiant temperature were recorded along with subject's physiological parameters (skin (T_sk) and oral temperature (T_c)) and subjective thermal sensation responses (TSV). From T_sk and T_c, body temperature (T_b) was evaluated. Subjective Thermal Sensation Vote (TSV) was recorded using ASHRAE 7-point scale. In PMV model, Fanger's T_sk equation was used to accommodate adaptive response. Stepwise regression analysis result showed T_b was better predictor of TSV than T_sk and T_c. Regional skin temperature response, suppressed sweating without dipping, lower sweating threshold temperature and higher cutaneous threshold for sweating were observed as thermal adaptive responses. These adaptive responses cannot be considered in PMV model. To incorporate subjective adaptive response, mean skin temperature (T_sk) is considered in dry heat loss calculation. Along with these, PMV-model and other two methodologies are adopted to calculate PMV values and results are compared. However, recent literature is limited to measure the sweat rate in Indians and consideration of constant Ersw in PMV model needs to be corrected. Using measured T_sk in PMV model (Method₁), thermal comfort zone corresponding to 0.5 ≤ PMV ≤ 0.5 was evaluated as (22.46–25.41) °C with neutral temperature of 23.91 °C, similarly while using TSV response, wider comfort zone was estimated as (23.25–26.32) °C with neutral temperature at 24.83 °C, which was further increased to with TSV-PPD_new relation. It was observed that PMV-model overestimated the actual thermal response. Interestingly, these subjects were found to be less sensitive to hot but more sensitive to cold. A new TSV-PPD relation (PPD_new) was obtained from the population distribution of TSV response with an asymmetric distribution of hot-cold thermal sensation response from Indians. The calculations of human thermal stress according to steady state energy balance models used on PMV model seem to be inadequate to evaluate human thermal sensation of Indians.     

车载自组网动态频谱分配技术研究进展

为了提高车载自组网的性能和频谱利用率,多接口动态频谱分配技术成为车载自组网的研究热点。介绍了车载自组网动态频谱分配算法的关键设计基础,对多接口多信道网络技术的发展现状进行了分类阐述,对车载自组网动态频谱分配技术的研究进行了展望。     

Gradient Estimation in Volume Data using 4D Linear Regression

In this paper a new gradient estimation method is presented which is based on linear regression. Previous contextual shading techniques try to fit an approximate function to a set of surface points in the neighborhood of a given voxel. Therefore a system of linear equations has to be solved using the computationally expensive Gaussian elimination. In contrast, our method approximates the density function itself in a local neighborhood with a 3D regression hyperplane. This approach also leads to a system of linear equations but we will show that it can be solved with an efficient convolution. Our method provides at each voxel location the normal vector and the translation of the regression hyperplane which are considered as a gradient and a filtered density value respectively. Therefore this technique can be used for surface smoothing and gradient estimation at the same time.     

On global-local artificial neural networks for function approximation

We present a hybrid radial basis function (RBF) sigmoid neural network with a three-step training algorithm that utilizes both global search and gradient descent training. The algorithm used is intended to identify global features of an input-output relationship before adding local detail to the approximating function. It aims to achieve efficient function approximation through the separate identification of aspects of a relationship that are expressed universally from those that vary only within particular regions of the input space. We test the effectiveness of our method using five regression tasks; four use synthetic datasets while the last problem uses real-world data on the wave overtopping of seawalls. It is shown that the hybrid architecture is often superior to architectures containing neurons of a single type in several ways: lower mean square errors are often achievable using fewer hidden neurons and with less need for regularization. Our global-local artificial neural network (GL-ANN) is also seen to compare favorably with both perceptron radial basis net and regression tree derived RBFs. A number of issues concerning the training of GL-ANNs are discussed: the use of regularization, the inclusion of a gradient descent optimization step, the choice of RBF spreads, model selection, and the development of appropriate stopping criteria.     

Maximizing Sparse Matrix–Vector Product Performance on RISC Based MIMD Computers

The matrix–vector product kernel can represent most of the computation in a gradient iterative solver. Thus, an efficient solver requires that the matrix–vector product kernel be fast. We show that standard approaches with Fortran or C may not deliver good performance and present a strategy involving managing the cache to improve the performance. As an example, using this approach we demonstrate that it is possible to achieve 2.5 times better performance over a Fortran implementation with an assembly coded kernel on an Intel i860. These issues are of general interest for all computer architectures but are particularly important for users of MIMD computers to achieve a useful fraction of the advertised peak performance of these machines.     

Nonlinear convective flow with variable thermal conductivity and Cattaneo-Christov heat flux

An analysis is introduced to investigate the salient features of nonlinear convective flow of thixotropic fluid in the version of Cattaneo-Christov heat flux theory. The stagnation point flow is present. The flow phenomenon is by an impermeable stretching sheet. The energy expression is modeled through the theory of Cattaneo-Christov heat flux. Characteristics of heat transfer phenomenon are described within the frame of variable thermal conductivity. Suitable variables reduced to the nonlinear partial differential expressions to the ordinary differential expressions. Series solutions of resulting systems are acquired within the frame of homotopy theory. Convergence analysis is achieved and suitable values are determined by capturing the so-called ℏ−curves. Graphical results for velocity and temperature are displayed and argued for sundry physical variables. Expression of skin friction coefficient is calculated through numerical values. Higher values of mixed convection parameter, Prandtl number, and thermal relaxation time lead to decay the temperature and layer thickness.

     

Harmonic point cloud orientation

In this work we propose a new method for estimating the normal orientation of unorganized point clouds. Consistent assignment of normal orientation is a challenging task in the presence of sharp features, nearby surface sheets, noise, undersampling, and missing data. Existing approaches, which consider local geometric properties often fail when operating on such point clouds as local neighborhood measures inherently face issues of robustness. Our approach circumvents these issues by orienting normals based on globally smooth functions defined on point clouds with measures that depend only on single points. More specifically, we consider harmonic functions, or functions which lie in the kernel of the point cloud Laplace-Beltrami operator. Each harmonic function in the set is used to define a gradient field over the point cloud. The problem of normal orientation is then cast as an assignment of cross-product ordering between gradient fields. Global smoothness ensures a highly consistent orientation, rendering our method extremely robust in the presence of imperfect point clouds.     

An unequal packet loss resilience scheme for video over the Internet

We present an unequal packet loss resilience scheme for robust transmission of video over the Internet. By jointly exploiting the unequal importance existing in different levels of syntax hierarchy in video coding schemes, GOP-level and Resynchronization-packet-level Integrated Protection (GRIP) is designed for joint unequal loss protection (ULP) in these two levels using forward error correction (FEC) across packets. Two algorithms are developed to achieve efficient FEC assignment for the proposed GRIP framework: a model-based FEC assignment algorithm and a heuristic FEC assignment algorithm. The model-based FEC assignment algorithm is to achieve optimal allocation of FEC codes based on a simple but effective performance metric, namely distortion-weighted expected length of error propagation, which is adopted to quantify the temporal propagation effect of packet loss on video quality degradation. The heuristic FEC assignment algorithm aims at providing a much simpler yet effective FEC assignment with little computational complexity. The proposed GRIP together with any of the two developed FEC assignment algorithms demonstrates strong robustness against burst packet losses with adaptation to different channel status.     

Fast and Error-Bounded Space-Variant Bilateral Filtering

The traditional space-invariant isotropic kernel utilized by a bilateral filter (BF) frequently leads to blurry edges and gradient reversal artifacts due to the existence of a large amount of outliers in the local averaging window. However, the efficient and accurate estimation of space-variant kernels which adapt to image structures, and the fast realization of the corresponding space-variant bilateral filtering are challenging problems. To address these problems, we present a space-variant BF (SVBF), and its linear time and error-bounded acceleration method. First, we accurately estimate spacevariant anisotropic kernels that vary with image structures in linear time through structure tensor and minimum spanning tree. Second, we perform SVBF in linear time using two error-bounded approximation methods, namely, low-rank tensor approximation via higher-order singular value decomposition and exponential sum approximation. Therefore, the proposed SVBF can efficiently achieve good edge-preserving results. We validate the advantages of the proposed filter in applications including: image denoising, image enhancement, and image focus editing. Experimental results demonstrate that our fast and error-bounded SVBF is superior to state-of-the-art methods.

     

Comparison of approaches for estimating reliability of individual regression predictions

The paper compares different approaches to estimate the reliability of individual predictions in regression. We compare the sensitivity-based reliability estimates developed in our previous work with four approaches found in the literature: variance of bagged models, local cross-validation, density estimation, and local modeling. By combining pairs of individual estimates, we compose a combined estimate that performs better than the individual estimates. We tested the estimates by running data from 28 domains through eight regression models: regression trees, linear regression, neural networks, bagging, support vector machines, locally weighted regression, random forests, and generalized additive model. The results demonstrate the potential of a sensitivity-based estimate, as well as the local modeling of prediction error with regression trees. Among the tested approaches, the best average performance was achieved by estimation using the bagging variance approach, which achieved the best performance with neural networks, bagging and locally weighted regression.     

A gradient flow approach to on-line robust pole assignment for synthesizing output feedback control systems

Pole assignment is a basic design method for synthesis of feedback control systems. In this paper, a gradient flow approach is presented for robust pole assignment in synthesizing output feedback control systems. The proposed approach is shown to be capable of synthesizing linear output feedback control systems via on-line robust pole assignment. Convergence of the gradient flow can be guaranteed. Moreover, with appropriate design parameters the gradient flow converges exponentially to an optimal solution to the robust pole assignment problem and the closed-loop control system based on the gradient flow is globally exponentially stable. These desired properties make it possible to apply the proposed approach to slowly time-varying linear control systems. Simulation results are shown to demonstrate the effectiveness and advantages of the proposed approach.