Intelligent control of via formation by photosensitive BCB forMCM-L/D applications

Via formation is a critical process sequence in multichip module (MCM) manufacturing, as it greatly impacts yield, density, and reliability. To achieve low-cost manufacturing, modeling, optimization, and control of via formation are crucial. In this paper, a model-based supervisory control algorithm is developed and applied to reduce undesirable behavior resulting from various process disturbances. A series of designed experiments are used to characterize the via formation workcell (which consists of the spin coat, soft bake, expose, develop, cure, and plasma descum unit process steps). The output characteristics considered are film thickness, uniformity, film retention, and via yield. Sequential neural network process models are used for system identification, and hybrid genetic algorithms are applied to synthesize process recipes. Computer simulation results show excellent control of output response shift and drift, resulting in a reduction of process variation. The performance limits of the supervisory control system are investigated based on these simulation results. The control algorithm is verified experimentally, and the results show 82.6, 64.4, and 17.3% improvements in maintaining target via yield, film thickness, and film uniformity, respectively, as compared to open loop operation     

A control-to-target architecture for process control

In this paper, we present an architecture for a run-to-run supervisory process control system that allows the engineer to tailor the form of control for specific processes. The architecture supports different degrees of control, from model-based control to statistical process control to diagnosis. The architecture is compatible with different techniques for model optimization, data acquisition and analysis, and model adjustment and feedback. A primary feature of this architecture is that engineers can define processes In terms of their desired effects, and use process models that transform those effects into machine settings. We have used object technology as the basis for our design and implementation of the architecture. Object-oriented modeling provides the flexibility required to support the varying degrees of control required in a large-scale manufacturing facility. In this paper, we define the components of the architecture, and describe in detail a process control system that was built with this architecture and used in the Computer Integrated manufacturing (CIM) system built for the Microelectronics Manufacturing Science and Technology (MMST) demonstration facility. Although the architecture was developed for the purpose of controlling semiconductor manufacturing processes, the principles behind the architecture may be applied to the control of any process     

微机-单片机分布式温度监控系统设计

在工业生产中，许多简单重复性操作或对人体有害的工作都由机器完成。这就需要相关设备来监视机器工作情况，井对机器的工作进行相应的控制。本文论述了PC-单片机分布式温度监控系统的实现方案，以及系统的硬件设计和软件设计方法。本系统充分利用了单片机价格低、功能强、抗干扰性能好等优点。本系统上位计算机采用Windows操作系统，可视化的界面方便用户操作。PC-单片机分布式温度监控系统利用MD转换芯片对现场温度进行转换，并把转换结果显示在监控计算机中。本系统采用RS232和RS422串口通信总线标准，将VB高级语言程序和分布式监控系统结合起来，构成了一个高效的温度监控网络系统。     

Statistical control of VLSI fabrication processes. II. A softwaresystem

For pt.I see ibid., vol.1, no.2, p.62-71, 1988. The algorithms used to implement the CMU-CAM statistical control system for VLSI integrated circuit fabrication are presented. The CMU-CAM system performs three major operations: modeling; quality control; and feed-forward control. In order to increase the efficiency of modeling and control, the problem is decomposed using statistical factorization techniques. Algorithms for process modeling and algorithms used in quality control and feed-forward control are described. The CMU-CAM system performs profit maximization through statistical process control. Its capabilities are illustrated by a number of computational examples     

Supervisory run-to-run control of polysilicon gate etch using insitu ellipsometry

Polysilicon gate etch is a critical manufacturing step in the manufacturing of MOS devices because it determines the tolerance limits on MOS circuit performance. The etch used in the current study suffers from machine aging, which causes processing results to drift with time. Performing the etch for the same time with fixed process setpoints (recipe) for all wafers would produce unsatisfactory results. Thus, an in situ ellipsometer was employed with a new run-to-run supervisory controller, termed predictor corrector control (PCC), to eliminate the impact of machine and process drift. A novel modeling technique was used to predict uniformity from the ellipsometry data collected at a single site on the wafer. Predictive models are employed by the PCC supervisory controller to generate optimal settings (recipe) for every wafer which will achieve a target mean etch rate, while maintaining a spatially uniform etch. A 200 wafer experiment was conducted to demonstrate the benefits of process control. Implementation of PCC resulted in a 36% decrease in standard deviation from target for the mean etch rate. In addition, the data indicates that controlling etch rate may improve the control and uniformity of the line width change     

电气监控系统在电厂升压站控制改造中应用与分析

针对某火电厂220 kV升压站控制室与#1机组为同一控制室这一特殊工况,解决#1机组实施监控系统改造,将#1机组原来的电气控制进入改造后的监控系统系统,将升压站的电气控制也接入改造后的监控系统.通过改用先进硬件设备,加强逻辑组态的严密性,重点处理好分散处理单元不间断供电以及输电线路继电保护重合闸充、放电逻辑时序配合等关键技术,确保升压站电气控制接入监控系统过程中及运行期间的平稳、安全、可靠.     

基于SOPC嵌入式系统的CCD摄像机设计与实现

设计并实现了一种基于EP3C40型FPGA的可编程片上系统(SOPC)嵌入式系统的CCD摄像机,使用FPGA完成CCD的驱动时序产生、数字图像统计处理、串口通讯、自动调光及自动增益、快速调光等控制功能.该摄像机具有高灵敏度、低噪声的特点,能适应外界光照条件的快速变化,适用于军事装备、科研以及监控等领域.     

Special purpose processor for parameter identification of CNC second order servo systems on a low-cost FPGA platform

The parameter identification process has an important role in servo systems tuning on computer numerical control (CNC) machine tools. The parameter identification is mostly done off-line due to the high computational load carried by the identification algorithms. In this work, we present a novel hardware architecture for an application specific processor implementing the recursive least-squares algorithm for the on-line identification. The developed processor is simulated and tested with the necessary elements to perform the CNC machine servo system control. The system was implemented in a low-cost FPGA, getting a maximum sample frequency of 40 kSps.     

Survey of research on application of heuristic algorithm in machine learning

Aiming at the problems existing in the application of machine learning algorithm,an optimization system of the machine learning model based on the heuristic algorithm was constructed.Firstly,the existing types of heuristic algorithms and the modeling process of heuristic algorithms were introduced.Then,the advantages of the heuristic algorithm were illustrated from its applications in machine learning,including the parameter and structure optimization of neural network and other machine learning algorithms,feature optimization,ensemble pruning,prototype optimization,weighted voting ensemble and kernel function learning.Finally,the heuristic algorithms and their development directions in the field of machine learning were given according to the actual needs.     

A dynamic approach to controlling high-level synthesis CAD tools

In this paper, we present a novel model of CAD tool control that can be used in the constraint-directed control of high-level synthesis tools. To enable this control we introduce the concept of a design space reasoning mechanism. We formally describe a statistical based, machine learning process that automatically generates the tool control knowledge necessary to drive the design space reasoning mechanism. The representation of this tool control knowledge in the form of a fuzzy, linear differential, qualitative model is described. Finally, the experimental results obtained using the Magellan system are presented     

Industrial applications of fuzzy logic at General Electric

Fuzzy logic control (FLC) technology has drastically reduced the development time and deployment cost for the synthesis of nonlinear controllers for dynamic systems. As a result we have experienced an increased number of FLC applications. We illustrate some of our efforts in FLC technology transfer, covering projects in turboshaft aircraft engine control, steam turbine startup, steam turbine cycling optimization, resonant converter power supply control, and data-induced modeling of the nonlinear relationship between process variables in a rolling mill stand. We compare these applications in a cost/complexity framework, and examine the driving factors that led to the use of FLCs in each application. We emphasize the role of fuzzy logic in developing supervisory controllers and in maintaining explicit tradeoff criteria used to manage multiple control strategies. Finally, we describe some of our FLC technology research efforts in automatic rule base tuning and generation, leading to a suite of programs for reinforcement learning, supervised learning, genetic algorithms, steepest descent algorithms, and rule clustering     

Automatic synthesis of equipment recipes from specified wafer-statetransitions

Run-to-run and supervisory control algorithms determine the equipment recipe to produce a desired output wafer state given the incoming wafer state and the current equipment model. For simple, low-dimensional equipment models, this problem is not difficult. However, when there are multiple responses for the system and the equipment models are nonlinear, automated synthesis of recipes is complicated by the potential for multiple solutions. While there are standard techniques for handling such inverse problems in general, each of these techniques is optimal only under certain conditions. We present a framework for performing automated synthesis of recipes that integrates database search, local optimization, and global optimization into a consistent methodology that is applicable to a wide range of equipment models and inversion problems in general. The integrated framework imposes quasi-continuity on the extracted recipes, is scalable to systems of high dimensionality, and can be optimized to minimize the expected synthesis time for any given problem. The framework has been implemented in a system that performs statistical optimization of CMOS transistor designs. The integrated framework provides a factor of 16 increase in performance over global optimization and a factor of three increase over exhaustive search and multiple starts of a local optimizer     

PLC和IPC组成的新型成缆机控制系统

该成缆机控制系统由日本欧姆龙公司的Ｃ２００ＨＳ型可编程控制器（ＰＬＣ）和台湾研华公司的工业控制计算机（ＩＰＣ６１０系列ＰＥＮＴＩＵＭ１６６主机）及其配套设备组成，实行两级监控。上位机采用中文Ｗｉｎ９５作操作系统，ＢｏｒｌａｎｄＣ＋＋５．０为编程工具，采用多线程编程方式。文章给出了ＰＬＣ控制的主要程序清单。该系统已运行一年多，效益良好。     

On universal simulation of information sources using training data

We consider the problem of universal simulation of an unknown random process, or information source, of a certain parametric family, given a training sequence from that source and given a limited budget of purely random bits. The goal is to generate another random sequence (of the same length or shorter), whose probability law is identical to that of the given training sequence, but with minimum statistical dependency (minimum mutual information) between the input training sequence and the output sequence. We derive lower bounds on the mutual information that are shown to he achievable by conceptually simple algorithms proposed here. We show that the behavior of the minimum achievable mutual information depends critically on the relative amount of random bits and on the lengths of the input sequence and the output sequence. While in the ordinary (nonuniversal) simulation problem, the number of random bits per symbol must exceed the entropy rate H of the source in order to simulate it faithfully, in the universal simulation problem considered here, faithful preservation of the probability law is not a problem, yet the same minimum rate of H random bits per symbol is still needed to essentially eliminate the statistical dependency between the input sequence and the output sequence. The results are extended to more general information measures.     

基于数据库和Internet的分布式远程多点监控系统研究

基于Internet的多监控点远程监控系统大多是单监控点系统的推广,这些多监控点系统只能对各测控点进行分时监控,不能对整个系统进行统一的调控,而且各测控点都需要固定的IP地址.为此,本文提出了一种基于数据库的Internet分布式多监控点远程监控系统解决方案.系统可对多监控点状态进行实时的监测,数据汇总、统计和分析和集中调控,各测控点不需要固定的IP地址;系统采用两级数据库实现了数据的缓存,有效解决了Internet网络数据传输不稳定的问题;系统设有Web服务器,通过WWW浏览器可对系统运行状况进行监控.本文给出了系统整体结构和系统各组成部分硬件、软件详细设计与具体实现.在校园网和城域网环境下对系统进行了实验测试.实验结果表明:系统可应用于中低速自动化监控领域,随着Internet网络数据传输速率的提高,系统监控的实时性将进一步得到提高.     

IC芯片检测的图像分割算法研究及其实现

全自动上芯机是用于芯片生产后工序的关键设备之一,而机器视觉检测系统又是提高该设备产能和质量的关键系统.在机器视觉检测系统中,图像的有效分割是图像分析的基础.针对摄像机采集到的图像由于镜头具有球面像差的原因都不同程度地存在图像降质的现象(如暗角),分析了基于迭代算法、改进的自适应门限以及同态滤波的图像分割算法,并对IC芯片图像进行了三种方法的实验比较,结果表明,改进的自适应门限的图像分割算法能够满足IC芯片检测高精度、高速度的要求.     

Iterative learning algorithms for linear Gaussian observation models

In this paper, we consider a signal/parameter estimation problem that is based on a linear model structure and a given setting of statistical models with unknown hyperparameters. We consider several combinations of Gaussian and Laplacian models. We develop iterative algorithms based on two typical machine learning methods - the evidence-based method and the integration-based method - to deal with the hyperparameters. We have applied the proposed algorithms to adaptive prediction and wavelet denoising. In linear prediction, we show that the proposed algorithms are efficient tools for tackling a difficult problem of adapting simultaneously the order and the coefficients of the predictor. In wavelet denoising, we show that by using the proposed algorithms, the noisy wavelet coefficients are subject to shrinkage and thresholding.     

HTTP malicious traffic detection method based on hybrid structure deep neural network

In response to the HTTP malicious traffic detection problem,a preprocessing method based on cutting mechanism and statistical association was proposed to perform statistical information correlation as well as normalization processing of traffic.Then,a hybrid neural network was proposed based on the combination of raw data and empirical feature engineering.It combined convolutional neural network (CNN) and multilayer perceptron (MLP) to process text and statistical information.The effect of the model was significantly improved compared with traditional machine learning algorithms (e.g.,SVM).The F₁value reached 99.38% and had a lower time complexity.At the same time,a data set consisting of more than 450 000 malicious traffic and more than 20 million non-malicious traffic was created.In addition,prototype system based on model was designed with detection precision of 98.1%~99.99% and recall rate of 97.2%~99.5%.The application is excellent in real network environment.     

MES系统与Wonderware平台通信的一种解决方法

Wonderware自动化系统平台是一个基于对象的工厂建模与业务逻辑开发平台,可满足工业自动化与信息人员对于数据采集、人机界面、逻辑控制等软件解决方案的要求,但是如果第三方应用比如MES程序或者PDA等移动终端想要获取系统平台中的实时数据则比较困难,本文中利用Wonderware提供的MXAccess工具包开发的MXGateWay组件可以轻松的解决这个问题,为其他应用程序访问自动化系统的实时数据搭起了一座桥梁。