Continuous hydrothermal processing of nano-crystalline particulates for chemical-mechanical planarization

A wide range of nano-crystalline, single and multi-component oxide/oxyhydroxide particulates, which may be potentially useful as abrasives for chemical-mechanical planarization (CMP) processes, have been produced using a novel, flow-through hydrothermal technology previously developed at the Pacific Northwest National Laboratory. The process, termed rapid thermal decomposition of precursors in solution (RTDS), converts aqueous feed stock solutions containing metal salts and other thermally activated reactants into suspensions or slurries of nano-crystals (with diameters of generally less than 30 nm) by continuous flow through a heated, high pressure reaction pipe (typically, 200–400°C, 6000–8000 psi). Flow at pressure is maintained using a nozzle at the down-stream end of the reaction tube. Crystallite formation occurs during the solution’s brief residence time (<30 s) in the reaction pipe. Control over crystalline phase and, in some cases, particle morphology can be tailored by selecting the appropriate feed chemistry and processing conditions. Using bench-scale equipment, RTDS is capable of producing nano-crystalline particulate material at rates of up to ≈500 gm of solids per hour. The RTDS processing and characterization of nano-crystalline zirconium-, titanium-, and iron-based oxide and oxyhydroxide particulates are reviewed.     

A modifying algorithm of the topological VLSI layer by dummy filling features based on modeling the chemical-mechanical planarization

The approach to modeling chemical-mechanical planarization (CMP) based on the notion of the effective filling density, which makes it possible to calculate the thickness distribution of the interlayer dielectric (ILD) on the surface of the VLSI crystal, is considered. The polynomial CMP model previously proposed in [1] is compared to model [2]. The algorithm of modifying the pattern of the VLSI topological layer by the dummy filling features, which was proposed in [3], is described using the proposed polynomial model. The results of model investigations of applying the developed modification algorithm of topological layers by examples of various VLSI types are presented.     

Mechanical and tribological properties of interlayer films for the damascene-Cu chemical-mechanical planarization process

Chemical-mechanical planarization (CMP) has emerged as the most preferred method to achieve excellent global and local planarity in the damascene-Cu process. As the feature sizes shrink, understanding the fundamentals of CMP is critical for successful implementation of the CMP process in sub 0.35-μm technology. It is also important to understand the effects of mechanical and tribological properties of the interlayer films on the CMP process to conduct successful evaluation and implementation of these materials. In this paper, we present the mechanical and tribological properties of various interlayer films (SiO₂, SiC, low-k B, low-k C, Ta, and Cu) and discuss the CMP process of the films in an alumina-based Cu slurry. Mechanical properties were evaluated using a nanoindentation technique. A micro-CMP tester was used to study the fundamental aspects of the CMP process. The coefficient of friction (COF) was measured during the process and was found to decrease both with downward pressure and with platen rotation. An acoustic sensor, attached to the substrate carrier, was used to monitor the process, and the signal was recorded to examine the difference in polishing behavior of these films. The acoustic emission (AE) signal was found to increase with the increase in platen velocity and pressure. Effects of machine parameters on the polishing behavior of the interlayer films and the correlation of mechanical properties with tribological properties have been discussed.     

Influence of slurry components on uniformity in copper chemical mechanical planarization

Many researchers studying copper chemical mechanical planarization (CMP) have been focused on mechanisms of copper removal using various chemicals. On the basis of these previous works, we studied the effect of slurry components on uniformity. Chemical mechanical planarization of copper was performed using citric acid (C₆H₈O₇), hydrogen peroxide (H₂O₂), colloidal silica, and benzotriazole (BTA, C₆H₄N₃H) as a complexing agent, an oxidizer, an abrasive, and a corrosion inhibitor, respectively. As citric acid was added to copper CMP slurry (pH 4) containing 3 vol% hydrogen peroxide and 3 wt% colloidal silica, the material removal (MRR) at the wafer center was higher than its edge. Hydrogen peroxide could not induce a remarkable change in the profile of MRR. Colloidal silica, used as an abrasive in copper CMP slurry containing 0.01 M of citric acid and 3 vol% of hydrogen peroxide, controlled the profile of MRR by abrading the wafer edge. BTA as a corrosion inhibitor decreased the MRR and seems to control the material removal around the wafer center. All the results of in this study showed that the MRR profile of copper CMP could be controlled by the contents of slurry components.     

基于模型辨识的神经网络PID控制在经纬仪中的应用

为了获得高精度和高速的位置控制,不确定参数如摩擦、惯量以及时延等都必须进行严格补偿,才能满足实时位置控制的需求.运用神经网络对非线性系统强大的自学习能力、记忆能力、计算能力以及各种智能处理能力,在控制系统中,能够学习和适应不确定性系统的动态特性,具有很强的容错性和鲁棒性,对经纬仪转台伺服系统进行在线辨识,得到系统的线性动态模型.利用此方法可以有效克服经纬仪的不确定因素,如电机参数的变化,负载转矩变化,摩擦的非线性变化等.在此基础上进行神经PID控制,PID参数依据系统特性进行在线调整,从而达到更好的控制效果和更强的鲁棒性,得到了仿真和试验验证.     

Neural network based adaptable control method for linearization of high power amplifiers

The proposed adaptable control method for linearization of high power amplifiers is powered by the neural networks technique that supports analogue polynomial type of predistorters, which are widely utilized in commercial power amplifiers for wireless communication purposes. This paper presents an algorithm to determine the coefficients by use of artificial neural networks and its generalization feature that helps to map the power amplifier response with optimal coefficients of the polynomial, which in a proper way pre-distorts an input signal of the amplifier. The concept of the predistortion has been introduced. Furthermore, the overall step-by-step initialization and functionality of the control process has also been described. The method has been tested successfully in a real power amplifier equipped with an analogue predistorting circuits. Presented measurement results imply that this approach is robust and well suited for such category of the power amplifier design, in which the artificial neural networks play substantial role.     

Device dependent control of chemical-mechanical polishing ofdielectric films

This paper presents a control scheme for run-to-run control of chemical-mechanical polishing (CMP). The control scheme tracks both device pattern dependent and equipment induced disturbances. The structure of the controller is such that sensitivity to qual (unpatterned blanket oxide) wafer frequency is minimized. Additionally, prethickness variation and metrology delay are accounted for in the design. Results from applying this scheme in volume production are presented     

非线性系统的神经网络自适应控制

给出了神经网络自适应控制方法,利用神经网络解决无参数,无模型等未知特性的系统控制问题,设计出神经网络自适应控制器,并对倒立摆系统进行仿真,验证理论的有效性和优越性。     

Neural network control for DC motor micromaneuvering

The application of a neural network controller for compensating the effects induced by the friction in a DC motor micromaneuvering system is considered in this article. A backpropagation neural network operating in the specialized learning mode, using the sign gradient descent algorithm, is employed. The input vector to the neural network controller consists of the time history of the motor angular shaft velocity within a prespecified time window. The on-line training of the neural network is performed in the region of interest of the output domain. The neural network output resembles that of a pulse width modulated controller. The effect of the number of neurons in the input and hidden layers on the transient system response is explored. Experimental studies are presented to indicate the effectiveness of the proposed algorithm     

Neural network control for dynamic voltage restorer

The dynamic voltage restorer is a power electronic device which has demonstrated its ability to protect sensitive loads from the effects of voltage sags. This compensator is connected in series with the distribution feeder. A neural network control is proposed. Simulation results are shown to validate these control methods.     

基于神经网络的欺骗式干扰类型识别

针对几种典型的欺骗式干扰信号,采用选择双谱作为分类的特征参数,并利用径向基神经网络分类器对其进行了识别。仿真结果表明,该方法在较低的干噪比(JNR)下仍具有较高的识别概率,能较好地实现对干扰信号的分类。     

Neural network impedance force control of robot manipulator

The performance of an impedance controller for robot force tracking is affected by the uncertainties in both the robot dynamic model and environment stiffness. The purpose of this paper is to improve the controller robustness by applying the neural network (NN) technique to compensate for the uncertainties in the robot model. NN control techniques are applied to two impedance control methods: torque-based and position-based impedance control, which are distinguished by the way of the impedance functions being implemented. A novel error signal is proposed for the NN training. In addition, a trajectory modification algorithm is developed to determine the reference trajectory when the environment stiffness is unknown. The robustness analysis of this algorithm to force sensor noise and inaccurate environment position measurement is also presented. The performances of the two NN impedance control schemes are compared by computer simulations. Simulation results based on a three-degrees-of-freedom robot show that highly robust position/force tracking can be achieved in the presence of large uncertainties and force sensor noise     

Neural network model for control of muscle force based on the sizeprinciple of motor unit

A neural-network model consisting of a single motor cortex output cell, α motoneurons, Renshaw cells, and muscle units is proposed. Linear relations between motor cortex output and force output that were observed in monkeys and firing rate versus force relations in human skeletal muscles are explained by computer simulations. It is suggested that Renshaw recurrent inhibition has the effect of linearizing the gain between motor cortex output and force output, and it is confirmed that the orderly recruitment of motor units is governed by unit size in accordance with a classical observation. The model appears to represent a fundamental neural mechanism of force control because both firing rates and forces calculated from the model agree with experimental data in humans and monkeys     

Neural network based approach to standard cell placement

Proposes a novel algorithm for placement of standard cells in VLSI circuits base don an analogy of this problem with neural networks. By employing some of the organising principles of these nets, the authors have attempted to improve the behaviour of the bipartitioning method as proposed by Kernighan and Lin. Their algorithm yields better quality placements compared with the above method, and also makes the final placement independent of the initial partition.<>     

Neural network based cell scheduling in ATM node

This letter deals with the queueing discipline for the superposition of arrival processes in the asynchronous transfer mode (ATM) case, particularly for the bursty traffic. A competitive algorithm, based on neural networks, for the cell scheduling is suggested. Computer simulations demonstrate that the new algorithm may be very useful in the bursty traffic case: the cell loss rate is at least one order of magnitude lower than in case of the classical round-robin discipline     

面向内容中心网络基于分布均匀度的蚁群路由策略

面向内容中心网络,采用转发服务评价函数得到转发端口集合,结合端口集合和自适应转发策略提出一种基于分布均匀度的层次化蚁群算法,通过感知路由信息素来选择最佳路径,综合考虑收敛时间和精确性对算法进行优化。仿真实验表明,所提出算法在降低内容请求时延和均衡节点负载方面有较好的表现。     

Neural network control of air-to-fuel ratio in a bi-fuel engine

In this paper, a neural network-based control system is proposed for fine control of the intake air/fuel ratio in a bi-fuel engine. This control system is an add-on module for an existing vehicle manufacturer's electronic control units (ECUs). Typically the ECU is calibrated for gasoline and provides a good control of the intake air/fuel ratio with gasoline. The neural network-based control system is developed to allow the conversion of a gasoline ECU to a bi-fuel form with compressed natural gas at minimal cost. The effectiveness of the neural control system is demonstrated by using a simulation of a Dodge four-stroke bi-fuel engine.     

基于线性自抗扰控制的永磁同步电机调速系统

针对自抗扰控制(ADRC)中需要整定参数较多,且无明确物理意义,只能采取试凑法的问题,提出了线性ADRC的控制方法,并将其应用于永磁同步电机的速度环中。线性ADRC需调整的参数大大减少,调节过程得以简化,且减少了计算量,便于硬件控制器实现,控制效果却与非线性ADRC相当。通过仿真试验,与PI控制和ADRC进行比较,可得出线性ADRC克服了非线性ADRC其参数调整难度大的缺点,保留了其静动态性能和鲁棒性较好的优点。     

Neural network based instant parameter prediction for wireless sensor network optimization models

Wireless Networks - Optimal operation configuration of a Wireless Sensor Network (WSN) can be determined by utilizing exact mathematical programming techniques such as Mixed Integer Programming...     

基于神经网络的模糊控制

随着科学技术的发展,传统的模糊控制已不能满足需要.以神经网络与模糊控制相结合为出发点,对其结构,特征进行了论述,并通过实例证明了其优越性.