Variable neighborhood search approaches for scheduling jobs on parallel machines with sequence-dependent setup times, precedence constraints, and ready times

In this paper, we discuss a scheduling problem for jobs on identical parallel machines. Ready times of the jobs, precedence constraints, and sequence-dependent setup times are considered. We are interested in minimizing the performance measure total weighted tardiness that is important for achieving good on-time delivery performance. Scheduling problems of this type appear as subproblems in decomposition approaches for large scale job shops with automated transport of the jobs as, for example, in semiconductor manufacturing. We suggest several variants of variable neighborhood search (VNS) schemes for this scheduling problem and compare their performance with the performance of a list based scheduling approach based on the Apparent Tardiness Cost with Setups and Ready Times (ATCSR) dispatching rule. Based on extensive computational experiments with randomly generated test instances we are able to show that the VNS approach clearly outperforms heuristics based on the ATCSR dispatching rule in many situations with respect to solution quality. When using the schedule obtained by ATCSR as an initial solution for VNS, then the entire scheme is also fast and can be used as a subproblem solution procedure for complex job shop decomposition approaches.     

CuO/SnO2-In2O3 sensor for monitoring CO concentration in a reducing atmosphere

The CO sensing property of CuO-loaded SnO₂-In₂O₃ sensor was investigated in a reducing atmosphere. The sensor response to CO for CuO/SnO₂-In₂O₃ (8/2) was much higher than that for CuO/SnO₂ in the range of 200-1000 ppm of CO concentration. Such a high sensor response of CuO/SnO₂-In₂O₃ may originate from the high dispersion of CuO playing a role as sensing site.     

Multiple-objective scheduling and real-time dispatching for the semiconductor manufacturing system

Implementing efficient scheduling and dispatching policies is a critical means to gain competitiveness for modern semiconductor manufacturing systems. In contemporary global market, a successful semiconductor manufacturer has to excel in multiple performance indices, consequently qualified scheduling approaches should provide efficient and holistic management of wafer products, information and manufacturing resources and make adaptive decisions based on real-time processing status to reach an overall optimized system performance. To cope with this challenge, a timed extended object-oriented Petri nets (EOPNs) based multiple-objective scheduling and real-time dispatching approach is proposed in this paper. Four performance objectives pursued by semiconductor manufacturers are integrated into a priority-ranking algorithm that serves as the initial scheduling guidance, and then all wafer lots will be dynamically dispatched by the hybrid real-time dispatching control system. A set of simulation experiments validate the proposed multiple-objective scheduling and real-time dispatching algorithm may achieve satisfactory performances.     

A discontinuous Galerkin solver for Boltzmann–Poisson systems in nano devices

In this paper, we present results of a discontinuous Galerkin (DG) scheme applied to deterministic computations of the transients for the Boltzmann–Poisson system describing electron transport in semiconductor devices. The collisional term models optical-phonon interactions which become dominant under strong energetic conditions corresponding to nano-scale active regions under applied bias. The proposed numerical technique is a finite element method using discontinuous piecewise polynomials as basis functions on unstructured meshes. It is applied to simulate hot electron transport in bulk silicon, in a silicon n⁺–n–n⁺ diode and in a double gated 12 nm MOSFET. Additionally, the obtained results are compared to those of a high order WENO scheme simulation and DSMC (Discrete Simulation Monte Carlo) solvers.     

基于局部与非局部线性判别分析和高斯混合模型动态集成的晶圆表面缺陷探测与识别

在复杂的半导体制造过程中,晶圆生产经过薄膜沉积、蚀刻、抛光等多项复杂的工序,制造过程中的异常波动都可能导致晶圆缺陷产生.晶圆表面的缺陷模式通常反映了半导体制造过程的各种异常问题,生产线上通过探测和识别晶圆表面缺陷,可及时判断制造过程故障源并进行在线调整,降低晶圆成品率损失.本文提出了基于一种流形学习算法与高斯混合模型动态集成的晶圆表面缺陷在线探测与识别模型.首先该模型开发了一种新型流形学习算法——局部与非局部线性判别分析法(Local and nonlocal linear discriminant analysis, LNLDA),通过融合数据局部/非局部信息以及局部/非局部惩罚信息,有效地提取高维晶圆特征数据的内在流形结构信息,以最大化数据不同簇样本的低维映射距离,保持特征数据中相同簇的低维几何结构.针对线上晶圆缺陷产生的随机性和复杂性,该模型对每种晶圆缺陷模式构建相应的高斯混合模型(Gaussian mixture model, GMM),提出了基于高斯混合模型动态集成的晶圆缺陷在线探测与识别方法.本文提出的模型成功地应用到实际半导体制造过程的晶圆表面缺陷在线探测与识别,在WM-811K晶圆数据库的实验结果验证了该模型的有效性与实用性.     

中束流离子注入机工艺实现原理

粒子高速冲击其他材料并进入其中叫作离子注入。在半导体工业中,这个过程用来向纯净衬底上掺杂杂质而形成半导体。半导体工艺中这个特别的步骤是由一种特殊的粒子加速装置完成,这就是离子注入机。     

A generalized Petri net modeling approach for the control of re-entrant flow semiconductor wafer fabrication

Re-entrant flow manufacturing lines, such as occur in semiconductor wafer fabrication, are characterized by a product routing that consists of multiple visits to a workstation or group of workstations during the manufacturing process. In this paper, a modeling approach is based on the use of generalized Petri nets for a re-entrant flow manufacturing line is presented. Specifically, three Petri net models representing a re-entrant flow line with three work centers and six machines are modeled. How these models may be used to represent a variety of queuing disciplines and work release policies is discussed.     

Study on CuO–BaTiO3 semiconductor CO2 sensor

The prototype of a CO₂ sensor made of CuO–BaTiO₃, which has capacitance sensitive effect, is designed based on the pn heterojunctions of CuO and BaTiO₃ semiconductors. The preparation of BaTiO₃ semiconductor powders is pointed out, using the coprecipitation and semiconducting techniques. The characteristic quantities relating to the capacitance sensitive effect of the sensor are studied systematically with the aid of a gas tester. A reasonable mechanism of the sensor is proposed.     

Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing

Since semiconductor manufacturing consists of hundreds of processes, a faulty wafer detection system, which allows for earlier detection of faulty wafers, is required. statistical process control (SPC) and virtual metrology (VM) have been used to detect faulty wafers. However, there are some limitations in that SPC requires linear, unimodal and single variable data and VM underestimates the deviations of predictors. In this paper, seven different machine learning-based novelty detection methods were employed to detect faulty wafers. The models were trained with Fault Detection and Classification (FDC) data to detect wafers having faulty metrology values. The real world semiconductor manufacturing data collected from a semiconductor fab were tested. Since the real world data have more than 150 input variables, we employed three different dimensionality reduction methods. The experimental results showed a high True Positive Rate (TPR). These results are promising enough to warrant further study.     

Electron beam evaporated carbon nanotube dispersed SnO2 thin film gas sensor

Carbon nanotube (CNT) is a useful material for gas-sensing applications because of its high surface to volume ratio structure. In this work, multi-wall CNTs are incorporated into tin oxide thin film by means of powder mixing and electron beam evaporation and the enhancement of gas-sensing properties is presented. The CNTs were combined with SnO₂powder with varying concentration in the range of 0.25–5% by weight and electron beam evaporated onto glass substrates. From AFM and TEM characterization, CNT inclusion in SnO₂thin film results in the production of circular cone protrusions of CNT clusters or single tube coated with SnO₂layer. Experimental results indicate that the sensitivity to ethanol of SnO₂thin film increases by the factors of 3 to 7, and the response time and recovery time were reduced by the factors of 2 or more with CNT inclusion. However, if the CNT concentration is too high, the sensitivity is decreased. Moreover, the CNT doped film can operate with good sensitivity and stability at a relatively low temperature of 250–300^∘C. The improved gas-sensing properties should be attributed to the increasing of surface adsorption area of metal oxide produced by CNT protrusion.