The central theme of this Special Issue is emerging opportunities and future directions in automation for green manufacturing, where information technology based modeling, analysis, control and optimization are the focus areas. The purpose is to show the state-of-the-art research and applications in the general area of automation in green manufacturing, by bringing together researchers and practitioners from both academia and industry, to address the significant advancement, expose the unsolved challenges, present the critical needs for integration with new technologies, and provide visions for future research and development. This Special Issue presents original, significant and visionary automation papers describing scientific models, methods and technologies with both solid theoretical development and practical importance that improve process, efficiency, productivity, quality, and reliability in green manufacturing. The contributions in this Special Issue can be divided into the following categories in green manufacturing: renewable energy products and systems; energy savings in manufacturing processes and systems; remanufacturing system design; and emission reduction in supply chain management. Specifically, the following papers are included in this Special Issue. The first category addresses renewable energy source, which includes manufacturing and operation of alternative energy products, such as batteries for electric vehicles, and system design to support manufacturing activities using renewable energy sources, such as electricity generated by wind turbines and solar panels. The second category focuses on energy savings in manufacturing, such as operational control and robot scheduling to minimize energy consumptions in production, optimal design of facility and production to reduce energy cost. The third category extends the study to remanufacturing, which plays a significant role to achieve sustainability and multiple life cycles. This includes remanufacturing system analysis, design and optimization, such as strategies for recycling, reassembly, etc. Finally, the last category considers supply chain management, within which multiple firms work together to reduce negative environmental impact.

Cluster tools are widely used as semiconductor manufacturing equipment. While throughput analysis and scheduling of single-cluster tools have been well-studied, research work on multicluster tools is still at an early stage. In this paper, we analyze steady-state throughput and scheduling of multicluster tools. We consider the case where all wafers follow the same visit flow within a multicluster tool. We propose a decomposition method that reduces a multicluster tool problem to multiple independent single-cluster tool problems. We then apply the existing and extended results of throughput and scheduling analysis for each single-cluster tool. Computation of lower-bound cycle time (fundamental period) is presented. Optimality conditions and robot schedules that realize such lower-bound values are then provided using ldquopullrdquo and ldquoswaprdquo strategies for single-blade and double-blade robots, respectively. For an -cluster tool, we present lower-bound cycle time computation and robot scheduling algorithms. The impact of buffer/process modules on throughput and robot schedules is also studied. A chemical vapor deposition tool is used as an example of multicluster tools to illustrate the decomposition method and algorithms. The numerical and experimental results demonstrate that the proposed decomposition approach provides a powerful method to analyze the throughput and robot schedules of multicluster tools.

Modeling and scheduling of cluster tools are critical to improving the productivity and to enhancing the design of wafer processing flows and equipment for semiconductor manufacturing. In this paper, we extend the decomposition methods in the work of Dwande et al. (2005) for multi-cluster tools with buffer/process modules (BPMs). The computation of the lower-bound cycle time (fundamental period) is presented. Optimality conditions and robot schedules that realize such lower-bound values are then provided using "pull" and "swap" strategies for single-blade and double-blade robots, respectively. The impact of BPMs on throughput and robot schedules is studied. It is found that such an impact depends on the BPM processing time and the cycle times of the decomposed clusters on both sides of BPMs. A chemical vapor deposition (CVD) tool is used as an example of multi-cluster tools to illustrate the proposed method, analysis, and algorithms. The numerical and experimental results demonstrate the effectiveness and efficiency of the algorithms.

This paper surveys applications of queueing theory for semiconductor manufacturing systems (SMSs). Due to sophisticated tool specifications and process flows in semiconductor manufacturing, queueing models can be very complicated. Research efforts have been on the improvement of model assumptions and model input, mainly in the first moment (averages) and the second moment (variations). However, practices show that implementation of classical queueing theory in semiconductor industry has been unsatisfactory. In this paper, open problems on queueing modeling of SMS are discussed. A potential solution is also proposed by relaxing the independent assumptions in the classical queueing theory. Cycle time reduction has constantly been a key focus of semiconductor manufacturing. Compared with simulation, queueing theory-based analytical modeling is much faster in estimating manufacturing system performance and providing more insights for performance improvement. Therefore, queueing modeling attracts generous semiconductor research grants. Unfortunately, existing queueing models focus on simple extensions of the classical queueing theory and fail to question its applicability to the complicated SMS. Hence, related researches have not been employed widely in the semiconductor industry. In this paper, we conduct a survey on the important works and also present some open problems. We also propose a novel solution by relaxing a key assumption in the classical queueing theory. We are currently funded by Intel to explore this potential solution, and we hope it can foster an interesting research field for the years to come.

In semiconductor assembly and test manufacturing (ATM), a station normally consists of multiple machines (maybe of different types) for a certain operation step. It is critical to optimize the utilization of ATM stations for productivity improvement. In this paper, we first formulate the bottleneck station scheduling problem, and then apply ant colony optimization (ACO) to solve it metaheuristically. The ACO is a biological-inspired optimization mechanism. It incorporates each ant agent's feedback information to collaboratively search for the good solutions. We develop the ACO-based scheduling framework and provide the system parameter tuning strategy. The system implementation at an Intel chipset factory demonstrates a significant machine conversion reduction comparing to a traditional scheduling approach.

The 11 papers in this special issue focus on e-manufacturing in the semiconductor industry.

Kits (such as accessories, fixtures, jigs, etc.) are widely used in production for many industries. They are normally product- and machine-specific, so a large kit inventory must be maintained when the product-mix variation is high. Fortunately, many kits are reconfigurable. That means they can be dissembled into components and then these components themselves (or together with some other components) can be reassembled into new types of kits. Therefore, we can save money and improve supply chain responsiveness by purchasing components instead of entire kits. However, research on capacity planning with reconfigurable kits has not been reported. We proposed a two-level hierarchical planning methodology to generate a complete capacity planning solution using mixed-integer linear programming. MaxIt covers mid-range monthly planning and automated capacity allocation system covers short-range weekly planning. These systems are integrated to generate optimal capacity plans considering kit components. This methodology has been successfully implemented in Intel's global semiconductor assembly and test manufacturing since 2004. In this paper, we present the hierarchical modeling framework and focus on MaxIt modeling with kit reconfiguration. We also verify the methodology by numerical experiments in a real production environment.

The initial resting pose of many industrial parts differs from the orientation desired for assembly. We show that it is possible to align parts during grasping using a standard parallel-jaw gripper. A solution is an arrangement of four gripper point contacts that will align the part in the vertical plane as the jaws close. Given a n-sided polygonal part and k uniformly distributed sample points on each side, we present an O(n/sup 4/k/sup 4/) numerical algorithm to compute a set of solutions or a report that no solution exists. The algorithm combines toppling, accessibility, and form-closure analysis. We have implemented the algorithm and report sensitivity data from physical experiments.

Assembly line reliability can be increased if grippers are carefully designed to capture and align parts. Jaw design depends on the geometric and mechanical properties of the part as well as its desired orientation. We propose a class of modular jaws based on rapidly machineable trapezoidal modules. An optimal jaw design is an arrangement of trapezoidal jaw modules that maximizes contacts between the gripper and the part at its desired final orientation over the constraints that: the jaws will capture and rotate the part to its desired orientation and achieve a form-closure grasp. Given the n-sided 2D convex projection of an extruded polygonal part, we develop an implemented O(n/sup 5/) algorithm to find the optimal jaw design. The algorithm combines toppling, jamming, liftoff, accessibility, and form-closure analysis. We verify resulting designs by physical experiments.

In previous work (2001), we identified a class of robot grippers that can orient and grasp parts using an arrangement of trapezoidal jaw modules. In this paper, we define a parametric tolerance class for the jaws, such that the part alignment is guaranteed for all jaw geometry in the class. This tolerance class is derived based on the analysis of toppling, motion trajectory, and form-closure. We describe the O(n log n) algorithm to compute the parametric tolerance class based on maximal and minimal jaw specifications. We implemented the algorithm and report the results of physical experiments.

We consider a sensorless approach to feeding parts on a conveyor belt using pins (rigid barriers) to topple parts into desired orientations. Given the 2D projection of an n-sided convex polyhedral part, its center of mass, and coefficients of friction, we give an O(n/sup 2/) algorithm to compute the "toppling graph", a new data structure that represents the mechanics of toppling, including rolling and jamming. The toppling graph can be used to identify critical pin heights that permit toppling. We compare pin heights predicted by the graph with physical experiments, and give a complete O(n/sup 3n/) algorithm for designing pin sequences.