Simulating conveyor-based AMHS layout configurations in small wafer lot manufacturing environments

A 300mm semiconductor fab requires a tremendous amount of investment with a great deal of process tools inside. Those process tools are connected by the automatic material handling system (AMHS), thus requiring an extremely complex AMHS network. Additionally, unlike simple products of an integrated device manufacturer (IDM) or a memory fab, foundry fab manufactures hundreds of consumer products simultaneously, subsequently creating globally an extremely high transportation volume and complex demands on the material handling system. Previously, experiential engineers designed AMHS, possibly taking months of design lead-time. However, the next generation fab AMHS design focuses not only AMHS specifications, but also operational know-how. Meanwhile, a long design lead time fails to fulfill the requirements of the fast ramp up plan. Additionally, design optimization and shrink lead time are achieved by applying simulation modeling as a design platform whenever TSMC constructs an AMHS for a new fab. Via this platform, precise simulation is performed on the AMHS specifications, fab layout, tool configuration, and process flow in-formation to ensure design success and avoid a potential AMHS bottleneck.

A 300mm semiconductor fab requires a tremendous amount of investment with a great deal of process tools inside. Those process tools are connected by the automatic material handling system (AMHS), thus requiring an extremely complex AMHS network. Additionally, unlike simple products of an integrated device manufacturer (IDM) or a memory fab, foundry fab manufactures hundreds of consumer products simultaneously, subsequently creating globally an extremely high transportation volume and complex demands on the material handling system. Previously, experiential engineers designed AMHS, possibly taking months of design lead-time. However, the next generation fab AMHS design focuses not only AMHS specifications, but also operational know-how. Meanwhile, a long design lead time fails to fulfill the requirements of the fast ramp up plan. Additionally, design optimization and shrink lead time are achieved by applying simulation modeling as a design platform whenever TSMC constructs an AMHS for a new fab. Via this platform, precise simulation is performed on the AMHS specifications, fab layout, tool configuration, and process flow in-formation to ensure design success and avoid a potential AMHS bottleneck.

In semiconductor manufacturing, most of 200mm fabrication facilities (called fabs) currently existing operate in manual or in semi-automatic material handling system. To remain competitive and to deal with the migration from high-volume/low-mix to high-mix/low-volume, most of these fabs require investment on factory automation especially on handling operations. This paper addresses a problem of Automated Material Handling System (AMHS) design for reticle in a photolithography area of 200mm wafer fab that was not originally designed to have such system. The objective is to define AMHS components: tracks layout, fleet size and management and reticle storing system while taking into account the existing area layout constraints (machine locations, storage area, corridors dimensions). A simulation approach is adopted to model the Automated Reticle Handling System (ARHS) and analyze the effect on key performance indicators. Using real instances, many simulation tests with alternative designs have been conducted which provided practical feedbacks.

To perform a high fidelity simulation study on a 300 mm wafer fabrication plant, modeling of the manufacturing process (MP) alone is not sufficient. Inclusion of the automated material handling system (AMHS) model is necessary due to the high degree of factory automation. There isn't, however, a single tool that is capable of modeling both the AMHS and MP with sufficient accuracy and granularity. A commercial simulation package such as AutoMod is usually used to model the AMHS while AutoSched AP is usually used to model the MP. These packages can be integrated using the supplied interoperation module but flexibility in optimizing the execution performance for different simulation models is lacking. In this paper, we present an approach to interoperation based on the High Level Architecture standard. We note that the typical characteristics of disparity in the models' time granularity and frequent model interactions are the obstacle to good execution performance.

We address the field of internal logistics, embodied in Automated Material Handling Systems (AMHSs), which are complex installations employed in sectors such as Baggage Handling, Physical Distribution, and Parcel & Postal. We work on designing an integral planning and real-time control architecture, and a set of generic algorithms for AMHSs. Planning and control of these systems need to be robust, and to yield close-to-optimal system performance. Currently, planning and control of AMHSs is highly customized and project specific. This has important drawbacks for at least two reasons. From a customer point of view, the environment and user requirements of systems may vary over time, yielding the need for adaptation of the planning and control procedures. From a systems’ deliverer point of view, an overall planning and control architecture that optimally exploits synergies between the different market sectors, and at the same time is flexible with respect to changing business parameters and objectives is highly valuable. An integral planning and control architecture should clearly describe the hierarchical framework of decisions to be taken at various levels, as well as the required information for decisions at each level, e.g., from overall workload planning to local traffic control. In this research, we identify synergies among the different sectors, and exploit these synergies to decompose AMHSs into functional modules that represent generic building blocks. Thereafter, we develop generic algorithms that achieve (near) optimal performance of the modules. As an example, we present a functional module from the Parcel & Postal sector. In this module, we study merge configurations of conveyor systems, and develop a generic priority-based real-time scheduling algorithm.KeywordsControl ArchitectureSpace UtilizationIntegral PlanningSpace AllocationWorkload BalanceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In the 300 mm AMHS with a connected loops layout, the vehicle can travel not just in one loop but all around the wide fab to execute tool-to-tool delivery. Determining how far the waiting FOUPs (WFs) or idle vehicles (IVs) should be considered before selecting a dispatching rule might make dispatching more efficient. A two-phase approach with simulation has been developed to assign the search range (SR) for studying this idea. The model of a simplified 300 mm AMHS was built using eM-Plant and Design Expert to design experiments and analyse the results. In phase I, the number of WF and IV in the system at the time of dispatching will affect the distance of the vehicle’s empty trip (DVemp). Further, the SR was assigned and evaluated based on the average and standard deviation of DVemp under different numbers of WF and IV in phase II. The results indicated that the SR significantly affects the performance, and a longer SR used in a light system is feasible; a shorter SR is applicable for a heavy system.

CyberOptics will be demonstrating the new In-Line Particle Sensor™ (IPS) as an extension of the industry-leading particle sensing technology. The new In-Line Particle Sensor with CyberSpectrum™ software detects particles in gas and vacuum lines 24/7 in semiconductor process equipment. The IPS quickly identifies, monitors and enables troubleshooting of particles down to 0.1μm. CyberOptics' ReticleSense® Auto Multi Sensor™ (AMSR) measure leveling, vibration, and relative humidity (RH) in an all-in-one wireless real-time device. The AMSR can capture multiple measurements in all locations – providing yet another way to increase yield and reduce downtime in semiconductor environments. The Airborne Particle Sensor™ (APSRQ) quickly monitors, identifies and enables troubleshooting of airborne particles down to 0.14µm within process equipment and automated material handling systems. CyberOptics Corporation is a leading global developer and manufacturer of high-precision 3D sensing technology solutions.CyberOptics' sensors are used for inspection and metrology in the SMT and semiconductor capital equipment markets to significantly improve yields and productivity. By leveraging its leading edge technologies, the Company has strategically established itself as a global leader in high precision 3D sensors, allowing CyberOptics to further increase its penetration of key vertical markets. Headquartered in Minneapolis, Minnesota, CyberOptics conducts worldwide operations through its facilities in North America, Asia and Europe. Visit www.cyberoptics.com for more information.

Wafer transport by Automated Material Handling Systems is essential in modern semiconductor front-end factories to efficiently supply the manufacturing tools. Most common are ceiling mounted Overhead Hoist Transport Systems that allow high throughput and fast delivery times. These systems are increasingly supplemented by various types of Automated Guided Vehicle Systems that enable high flexibility. This development requires new approaches for transport system dispatching.We denote such Automated Material Handling Systems that combine several types of transport systems as heterogeneous. Here, vehicles with different characteristics can execute either a complete or a part of a transport order. Until now, this task allocation process for the vehicles is executed based on predefined rules. A dynamic exchange between the transport systems based on the current system state is not yet considered.This paper presents a formalization of the related planning problem by a mathematical model for transport system scheduling. The approach is not suitable for real-time decision making. However, on the one hand, it may help to identify and evaluate the solution quality of heuristic approaches. On the other hand, it allows the evaluation of the effect of transfers.

Throughput analysis in an automated material handling system, such as an Automated Storage and Retrieval System(AS/RS), may be a complex problem. In the past, several approaches have been used for such an analysis. This paperpresents a combinatorial ap proach to evaluating the throughput performance of a mini-load system, with simulation as the primary method of investigation.

We present key approaches and methodologies from Intel's 300 mm factory integration efforts. The topics discussed in this paper are aimed at cross-functional optimization of fab layouts, automated material handling systems (AMHS) and operations. We discuss the key benefits of up front understanding of the interactions and investigate specific attributes in layouts and AMHS configurations where the integrated design/analysis is addressing cost-benefits and flexibility trade-offs needed in the high volume 300 mm factories.

This paper proposes an analytical model useful in the design of conveyor-based automated material handling systems (AMHS) to support semiconductor manufacturing. The objective is to correctly estimate the work-in-process on the conveyor and assess the system stability. The analysis approach is based on a queuing model, but takes into account details of the operation of the AMHS including turntables. A numerical example is provided to demonstrate and validate the queuing model over a wide range of operating scenarios. The results indicated that the analytical model estimates the expected work-in-process on the conveyor with reasonable accuracy.

The automated material handling system (AMHS) in semiconductor industry plays a vital role in reducing wafer cycle times and enhancing fabrication plant productivity. Vehicle allocation for the AMHS is a challenging task because of the complexity of the manufacturing process and the stochasticity introduced by the inherent variability of processing times. This situation is observed especially in 300 mm wafer fabrication plants where the AMHS comprises both interbay and intrabay systems that perform the timely deliveries. To address this issue, we use the optimal computing budget allocation (OCBA) and extend it by adding genetic algorithm (GA). Under this combined approached, the number of iterations of each alternative is determined by OCBA, and then the optimal solution in the domain of feasible solutions is found through GA. This research provides a useful reference for both scholars and practitioners seeking optimal allocation of lithographical resources and number of iterations using random parameters.

Intergrated factory models of semiconductor fabrication facilities allow conclusions to be drawn on the impact of a given Automated Material Handling System (AMHS) and interactions between material flow and factory performance. A generic model of a 300mm wafer fabrication facility has been built to support decisions to be made in terms of dimensioning of the potential AMHS solutions.

A performance model of automated material handling systems with double closed-loops and shortcuts in 300 mm semiconductor wafer fabrication systems

Several manufacturing industries try to reduce transportation waste using automated material handling systems, which can enhance the transportation of raw materials from one location to another in the production line of a manufacturing area. The issue with transportation and job flow is a critical factor in a production line because some production stations need to wait for the work-in-progress to be delivered. Automated guided vehicle (AGV) transportation needs a setup of traffic control over a factory’s physical infrastructure and simulation. Doing so can help showcase and evaluate possible deficiencies that can be improved in the real job flow scenario of the production line. The design of experiment plays a huge role in finding and explaining variations of information under conditions that are regularly put as a hypothesis to reflect or describe the variation. A simulation model is implemented by adopting simplified AGV parameters. The model development follows the structure of system specification → machine specification → AGV specification → discrete-event simulation model → experimental design → analysis of performance indicators (PIs). To precisely reflect an alternative for evaluating aforementioned issues, this study proposes the model stated above and an analysis that is based on the PIs. Analysis of variance (ANOVA) results are chosen to analyze different factors affecting the PIs. Using the factorial ANOVA test results, this study uses one-way and two-way interactions to compare the relationship between job flow time, AGVs, AGV utilization, number of AGVs, and average waiting time.