Modeling and optimization of semiconductor manufacturing process with neural networks

A neural network-based process model is proposed to optimize the semiconductor manufacturing process. Being different from some works in several research groups which developed neural network-based models to predict process quality with a set of process variables of only single manufacturing step, we applied this model to wafer fabrication parameters control and wafer lot yield optimization. The original data are collected from a wafer fabrication line, including technological parameters and wafer test results. The wafer lot yield is taken as the optimization target. Learning from historical technological records and wafer test results, the model can predict the wafer yield. To eliminate the "bad" or noisy samples from the sample set, an experimental method was used to determine the number of hidden units so that both good learning ability and prediction capability can be obtained.

Numerical Simulation of Heat Transfer and Kinetics in the Bulk Growth of Silicon Carbide

The growth process of 2-inch silicon carbide (SiC) single crystals by the physical vapor transport method (or modified Lely method) has been modeled and simulated. The comprehensive process model incorporates the calculations of radio frequency (RF) induction heating, heat and mass transfer and growth kinetics. The transport equations for electromagnetic field, heat transfer, and species transport are solved using a finite volume-based numerical scheme called MASTRAPP (Multizone Adaptive Scheme for Transport and Phase Change Process). Temperature distribution for a 2-inch growth system is calculated, and the effects of induction heating frequency and current on the temperature distribution and growth rate are investigated. The predicted results have been compared with the experimental data.

一种基于Agent的自适应软件过程模型

传统的软件过程模型大多是静态的、机械的、被动的,它们要求软件工程人员在描述软件过程时预期所有可能发生的情况,并且显式地定义这些问题的解决方案.当软件过程所处的环境发生变化时,软件过程无法自适应地对这些变更作出相应的调整.提出了一种基于Agent的自适应软件过程模型.在这种软件过程模型中,软件过程被描述为一组相互独立而对等的实体——软件过程Agent.这些软件过程Agent能够对软件过程环境的变化主动地、自治地作出反应,动态地确定和变更其行为以实现软件开发的目标.

一种基于模型融合的CMM实施过程建模方法

提出了一种基于模型融合的CMM实施过程建模方法.该方法使用软件过程工程元模型SPEM建立CMM过程模型CPM和企业过程模型EPM,通过融合CPM和EPM来获得CMM实施过程模型C IPM.文中利用带标记的有向图描述过程模型,给出了模型融合方法,并进行了一致性证明.最后通过一个过程模型融合原型工具和实例说明了方法的应用情况.

基于模拟的企业过程模型自动优化技术研究

企业过程模型参数自动优化是一个多参数多目标的系统优化问题.采用线性加权法将其转换成多参数单目标问题的求解,提出基于计算机模拟的企业过程模型参数的自动寻优方法.它将传统的共轭梯度法FR（fletcher reeves）和禁忌搜索算法TS（tabu search）结合起来,采用FR法进行局部寻优,由TS法实现从当前局部最优点向全域范围内的更优区域转移,循环往复达到求出全域范围最优点的目的.改进了FR法与TS法,克服了各自的缺陷,并提出禁忌区域表的概念,从而加速寻优过程.它适用于任意多维曲面的多极值问题最优求解,对企业进行BPR（business process reengineering）和实施ERP（enterprise resource planning）管理有较大的指导意义.

一种基于XML的工作流过程模型的设计和实现

1.引言面对数字经济时代带来的不断增长的竞争压力,企业逐步寻求新的商业运作模式以适应这种变化。随着信息技术和Internet技术的迅猛发展,为适应这种变化提供了条件。企业不再满足于孤立、零散的办公自动化和计算机应用,而是需要综合的、集成化的解决方案。作为一种对常规性事务进行管理、集成的技术,工作流管理系统(Workflow ManagementSystem,WMS)的出现是必然的。如何建立企业业务过程模型是工作流管理系统的核心问题。

用计算机辅助创新软件解决产品技术创新问题的研究

本文简要介绍了发明问题解决理论TRIZ和机算机辅助创新软件Pro/Innovator的主要内容,建立了基于创新理论TRIZ和CAI软件的创新问题解决流程模型,作为特例建立了Pro/Innovator1.0的简化流程图,最后用软件完成了一个工程实例问题的创新设计。

基于智能可重构装配线制造系统的产品装配序列规划研究

研究了基于智能装配单元的可重构装配线制造系统环境中的产品装配建模方法与装配序列表达机制,提出由产品层次装配模型与生产线装配工艺齿状结构描述相结合的重构装配线装配序列规划与表达方法。

基于PMAC开放式数控系统软件开发

基于PC和多轴运动控制器的开放式数控系统是理想的开放式数控系统。介绍了基于PMAC的开放式数控系统结构形式,PMAC的差补、位置控制、伺服功能、以PMAC和PC机为硬件平台搭建了数控系统,并对其硬件构成和软件设计结构进行了分析。着重从软件设计的角度,介绍了PTALK控件的功能和作用,对数控系统软件构成进行了详细的阐述。并设计出了友好的用户界面,在实际应用中具有重要意义。

卷烟厂制丝线生产过程建模与计划仿真

对当前卷烟厂制丝线计划与调度管理中存在的不足进行了介绍,并在建立制丝生产线生产过程模型的基础上,提出了用于解决问题的计划仿真系统,对所采用的仿真策略进行了详细介绍。采用这一策略实现的仿真系统能够很方便的适应于其他行业类型的仿真。

基于时间Petri网的制造过程模型研究

分析了时间Petri网的激发规则、托肯可用时间和抑制弧等特性,以及制造过程中随机故障的特征。提出不同的时间关联方式对应的多种建模方法,考虑不同的故障发现模式、不同的作业处理策略,建立相应的单机制造过程模型。在此基础上采用模块化和层次化方法可以构建复杂制造过程的时间着色Petri网模型,并可以转换成仿真模型,进一步分析随机机器故障对制造过程性能的影响。

黄骅坳陷大港探区油气资源潜力与资源结构预测

Facing the problems that Dagang region of Huanghua Depression has high exploration degree and its remaining resource potential and structure are not clear, the theory of Petroleum Accumulation System (PAS) is applied to divide and evaluate the oil/gas systems quantitatively. Then, the petroleum accumulation systems are taken as units to forecast and analyse the oil/gas resources and their structure using statistical methods of sampling analysis of discovery process model and generalized pareto distribution model. The exploration benefit of the unit is estimated using exploration simulation methods. On the basis of the above study, the resource potential of Huanghua Depression is discussed.Huanghua Depression can be diveded into four petroleum accumulation systems, i.e. North PAS5 Middle Qibei PAS, Middle Qinan PAS and South PAS. Each PAS can be diveded futher into several sub- PASs. Using the basic princple of Analytical Hierarchy Process, the method of quantitative evaluation of PAS is established. Then the elements and maturity of PAS are evaluated quantitatively.Taking migration and accumulation units and sub-PASs as prediction units, sampling analysis of discovery process model and generalized pareto distribution model are applied comparatively to forecast the resource structure of eight migration and accumulation units in six PASs of medium-high exploration degree. The results of these two methods are contrasted and analyzed. An examination of X2 data of these two models from exploration samples shows that generalized pareto distribution model is more effective than sampling analysis of discovery process model in Huanghua Depression. It is concluded that minimum and maximum size of reservoir and discovery sequence of reservoirs are the sensitive parameters of these two methods.Aiming at the difficult problem of forecast in low exploration degree, by analysis of relativity between resource parameters and their possible influential geological factors, forecast models for resource parameters were established by liner regressing. Then the resource structure is forecasted in PASs of low exploration degree.Based on the forecast results, beginning with the analysis of exploration history and benefit variation, the exploration benefit variation of the above PASs is fitted effectively using exploration simulation method. The single well exploration benefit of remaining oil resource is also forecasted reasonably.The results of resource forecast show that the total oil resources ofHuanghua Depression amount to 2.28 b illion ton. By the end o f 2 003, the accumulative total proved oil reserve is 0.90 billion ton and the remaining oil resources is 1.38 billion ton. The remaining oil resource is concentrated in Kongdian-Dengmingshi, Banqiao-Beidagang, Qidong-Yangerzhuang and Baidong-Qizhong sub-PASs.

工作幸福感的结构、测量及作用机制

With the emergence and development of positive psychology, happiness has been the focus of academia and business. However, there is no uniform measure of happiness, because of many different theories of happiness, which are not compatible with others. It bounds the further development of happiness theory. It is also the same with the research of work well-being, which refers to the emotional experience and quality of psychological functioning of employee in the workplace. Subjective well-being (SWB) and psychological well-being (PWB) are two major theories of happiness. Prior research has demonstrated the integration of these two theories theoretically, but still needs more empirical support. Besides, in line with the development of positive psychology, a body of knowledge about positive leadership is advocated. Transformational leadership is treated as one kind of positive leadership, since it emphasizes the leader’s motivational and elevating effect on followers. But the extent to which the transformational leadership can enhance work well-being, and what the mechanism is, these are the questions need to be explored. Based on the integration of SWB and PWB, this research tried to investigate the structure, measurement and mechanism of work well-being, and combining with the theory of transformational leadership, this study also tried to investigate the relationship between transformational leadership and work well-being. The structure and measurement of work well-being, the relationships between work well-being and job characteristics (including job resources and job demands), the relationships among transformational leadership, job resources, work well-being and corresponding outcomes, the relationships among transformational leadership, job demands, work well-being and corresponding outcomes, and the relationships among transformational leadership, group job characteristics, group work well-being and corresponding group outcomes were explored by using content analysis, Subject Matter Experts (SMEs) discussion, and structural questionnaire surveys. More than 7000 subjects were surveyed, and Explore Factor Analysis (EFA), Confirm Factor Analysis (CFA), Structural Equation Modeling (SEM), Hierarchical Linear Modeling (HLM) and other statistics methods were used. The following is the major conclusions. Firstly, work well-being is a two high-order factors structure, which includes affective well-being (AWB) and cognitive well-being (CWB). AWB is similar to SWB, and CWB is similar to PWB. Besides, the construct of AWB includes sub-dimensions of positive emotional experience and negative emotional experience. And the construct of CWB consists of work autonomy, personal growth, work competent, and work significance. Secondly, the relationships between job characteristics and AWB and CWB are different. On one hand job demands are directly related to AWB, and are indirectly related to CWB through the full mediation of AWB, on the other job resources are directly related to CWB, and are indirectly related to AWB through the full mediation of CWB, which means AWB and CWB reciprocally influences each other in the model of job demands-resources. These results were concluded as the process model of work well-being. Thirdly, AWB and CWB are positively related to many workplace outcomes, including job satisfaction, group satisfaction, organizational commitment, turnover intention, job performance, organizational citizenship behavior (OCB), and general psychological health and general physiological health. Fourthly, transformational leadership is indirectly related to CWB through the full mediation of job resources, and is related to AWB through the partial mediation of job demands. Meanwhile, transformational leadership is related to many workplace outcomes through the mediation of job characteristics and work well-being. These results implied that transformational leadership is indeed one kind of positive leadership. Fifthly, in the group level, transformational leadership is indirectly related to group CWB through the full mediation of group job resources, and is related to group AWB through the full mediation of group job demands. Group AWB has positive influence on group CWB, but not vice versa. Group job characteristics and group work well-being fully mediate the relationships between transformational leadership and intragroup cooperation and group performance.