TOWARDS A SUPPLY CHAIN SIMULATION REFERENCE MODEL FOR THE SEMICONDUCTOR INDUSTRY

Proceedngs of the 2011 Wnter Smulaton Conference S. Jan, R.R. Creasey, J. Hmmelspach, K.P. Whte, and M. Fu, eds. TOWARDS A SUPPLY CHAIN SIMULATION REFERENCE MODEL FOR THE SEMICONDUCTOR INDUSTRY Hans Ehm Infneon Technologes AG Supply Chan Management Semconductor Industry Munch, 81726, GERMANY Hanna Wenke Lars Mönch Unversty of Hagen Department of Mathematcs and Computer Scence Unverstätsstraße 1 Hagen, 58097, GERMANY Thomas Ponsgnon Infneon Technologes Ireland Ltd. Supply Chan Management New Street Dubln 8, IRELAND Lsa Forstner Infneon Technologes AG Supply Chan Management Semconductor Industry Munch, 81726, GERMANY ABSTRACT In ths paper, we descrbe major steps to buld a supply chan smulaton reference model for the semconductor ndustry. We start by dentfyng requrements for such a reference model. Then we dentfy the man buldng blocks of the model. We present a technque to deal wth load-dependent cycle tmes n sngle front-end and back-end facltes and n the overall network to reduce the modelng and computatonal burden. The qualty of ths reducton technque s assessed by comparng the full model and the model wth a reduced level of detal. Fnally, we dscuss several potental applcaton scenaros for a smulaton reference model of a semconductor supply network. 1 INTRODUCTION A set of very complex manufacturng processes s the heart of semconductor manufacturng. A semconductor chp s a hghly mnaturzed, ntegrated crcut (IC) consstng of thousands of components. Semconductor manufacturng starts wth thn dscs, called wafers, made of slcon. A large number of usually dentcal chps can be produced on each wafer by fabrcatng the ICs layer by layer n a wafer fabrcaton faclty (wafer fab). The correspondng step s referred to as the Fab step. Next, electrcal tests that dentfy the ndvdual des that are lkely to fal when packaged are performed n the Probe faclty. An electronc map of the condton of each de s made so that only the good ones wll be put nto a package. The probed wafers are then sent to an Assembly faclty where the good des are put nto an approprate package. Fnally, the assembled des are sent to a test faclty where they are tested n order to ensure that only good products are sent to customers. Wafer fabrcaton and probe are often called the front-end and assembly and test are called the backend. The current generaton of semconductor products often requres up to 700 unt processng steps that can take up to four months to complete. Supply chan management (SCM) ssues have become more and more mportant n the last decade (Chen et al. 2011). Ths was caused by the fact that front-end operatons are often performed n hghly 978-1-4577-2109-0/11/$26.00 2011 IEEE 2124

ndustralzed natons, whle back-end operatons are typcally carred out n countres where labor rates are cheaper. In addton, today there are centers of competences for wafer fab, probe, assembly, test, or sometmes only sngle process steps wthn the stes of companes, slcon foundres, or subcontractors. These centers of competences speed up nnovatons and reduce costs, but ncrease the complexty of supply chan management. The semconductor ndustry s captal ntensve caused by extremely expensve machnes. The manufacturng process s very complex due to the reentrant flows n combnaton wth very long cycle tmes and the dfferent levels of uncertantes nvolved. Capacty expansons are very expensve and tmeconsumng. Ths knd of decson s based on demand forecast for the next years. Because of the rapdly changng envronment, the demand s very volatle. Consequently, the forecast s rarely accurate. Ths characterzaton of semconductor manufacturng leads to the concluson that the semconductor ndustry s an extreme feld for SCM solutons from an algorthmc and also from a software pont of vew (Chen et al. 2011). There are reference (smulaton) models for sngle wafer fabs (MASM 1997), manly developed n the Measurement and Improvement of Manufacturng Capacty (MIMAC) project (Fowler and Robnson 1995) that are used by many academc researchers workng wth the semconductor ndustry. A second, wdely used model s the MnFab model, proposed by researchers at Intel (Sper and Kempf 1996). It s a low complexty smulaton model that mmcs the behavor of a wafer fab by contanng reentrant process flows, batchng tools, and sgnfcant sequence-dependent setup tmes. At the same tme, such reference models for smulaton are not avalable for supply chans n the semconductor ndustry. In the present paper, we descrbe some ntal steps to come up wth a set of such reference models. The paper s organzed as follows. In the next sesson we descrbe supply chan smulaton ssues n the semconductor ndustry. Moreover, we also dscuss related lterature. In Secton 3, we descrbe the requrements for a supply chan (smulaton) reference model and ntroduce the man buldng blocks of such a model. Results of smulaton modelng for the base system of the supply chan reference model are presented n Secton 4. Fnally, we descrbe some possble applcaton scenaros n Secton 5. 2 SUPPLY CHAIN SIMULATION AND RELATED LITERATURE There are many papers that deal wth supply chan smulaton n ndustres dfferent from semconductor manufacturng (Ingalls 1999, Schunk and Plott 2000, Chang and Makatsors 2001, amongst others). Smulaton s well accepted as a tool to support the desgn and control of supply chans. It s shown by Klejnen (2005) that dscrete-event smulaton s an mportant technque to smulate the base system of supply chans, whle system dynamcs s used to represent the correspondng plannng and control systems. Jan et al. (1999) dscuss the crtcalty of detaled modelng for smulatng semconductor supply chans. A supply chan model consstng of four wafer fabs and one assembly and test faclty s consdered. Fully detaled models are compared wth reduced smulaton models where only bottlenecks are modeled n detal. They conclude that fully detaled smulaton models are needed. However, long computng tmes are reported for such models. At the same tme, the modelng effort s also large. Model reducton technques based on aggregated process flows for a sngle wafer fab are also dscussed by Hung and Leachman (1999) n the context of teratve smulaton. Because of the huge modelng effort that s needed to fnd approprated aggregated routes we do not take ths approach. There s another stream of research that deals wth dstrbuted smulaton to perform smulaton studes for supply chans n the semconductor ndustry. Lendermann et al. (2003) dscuss a scenaro that contans two wafer fab smulaton models, one smulaton model of an assembly and test faclty, one warehouse, and fnally one dstrbuton center. The Hgh-Level-Archtecture (HLA) s used to couple the dfferent smulaton models, called federates. A smlar approach was proposed by Chong et al. (2006). Gan et al. (2007) also use HLA n a borderless wafer fab scenaro. However, because of the techncal dffcultes of HLA and because of the large modelng effort for each sngle federate we do not thnk that ths approach s approprate to model supply chans n the semconductor ndustry. 2125

A strct separaton between plannng system, control system, and base system of a supply chan s dscussed n Goddng, Sarjoughan, and Kempf (2003). Dscrete Event System Specfcatons are used to model the base system of the supply chan. Ths approach was refned n Huang et al. (2009). Here, model predctve control s used wthn the plannng and the control system of the supply chan. But agan, only very smple supply chans are modeled and smulated. Jan (2006) propose a conceptual framework for supply chan modelng and smulaton that s based, at least n parts, on the Supply Chan Operatonal Reference (SCOR) model. However, t seems that the framework proposed by Jan s too generc and does not nclude enough specfc detals for smulatng supply chans of the semconductor ndustry. Duarte et al. (2007) propose a compact abstracton of a sngle manufacturng node n a semconductor supply network. However, the case of an entre supply chan contanng dfferent nodes s not addressed n ths paper. In ths paper, we wll extend ths approach to model entre supply networks. We conclude that no supply chan smulaton reference models, n the sense of a test-bed, are avalable n the semconductor ndustry. However, such reference models are hghly desrable because they allow to model the dynamcs of the correspondng supply chans. Furthermore, such models, f publcly avalable on the web, would allow a far comparson of plannng algorthms proposed by dfferent researchers wthout spendng much effort to buld a smulaton model from scratch. 3 BUILDING BLOCKS OF A SIMULATION REFERENCE MODEL In ths secton, we start by collectng requrements for a supply chan smulaton reference model n the semconductor ndustry. Then, we descrbe the base system used. We also dscuss whch parts of the plannng system and control system should be ncluded n the reference model. 3.1 Requrements of a Reference Model In ths subsecton, we derve several requrements for a supply chan reference model. Of course, our pont of vew s nfluenced to a certan degree by the smulaton reference models for sngle wafer fabs provded by the MASM Lab and by SEMATECH (MASM 1997) wthn the MIMAC project. We refer to these models as MIMAC models n the remander of ths paper. We derve requrements based on the nsght that each supply chan conssts of a plannng and control system and process and a base system and process. The plannng and control system s responsble for decson-makng. It determnes how many wafers have to start n whch perod of tme, how much materal has to be released from each nventory pont, and fnally where to shp the fnal ICs. The plannng and control process s responsble for usng the plannng and control system. The base system conssts of the resources. It s responsble for the physcal flow of materal through the supply network. It s possble to determne when a certan IC s completed and shpped,.e., when customer orders are fulflled and how much nventory s n each node of the supply network at a certan pont of tme usng data of the base system. The base process descrbes how resources are used by workng objects. In a certan sense, products nfluence the base process. Customers are responsble for orders and demand. They are consdered as external enttes that nfluence the plannng and control system and at the same tme the base system. We come up wth the followng requrements: 1. The reference model should contan a base system that s typcal for a semconductor supply network. A base system that s as smple as possble s hghly desrable. It has to contan mportant enttes that represent resources and ther stochastc behavor. An approprate level of detal has to be chosen that makes meanngful plannng and control decsons possble. An approprate modelng of load-dependent cycle tmes s crucal. 2. Products have to be ncluded nto the reference model to represent the base process. 3. The model should contan customers that are responsble for order generaton. 4. The reference model has to contan demand nformaton that allows plannng decsons n connecton wth the orders of the customers. The relatonshp of demand and frm orders has to be ncor- 2126

porated nto the reference model. Approprate stochastc demand patterns have to be ncluded n the reference model. 5. The reference model has to contan a smple plannng and control system that determnes wafer starts based on the frm orders and the gven demand. 6. It s mportant to nclude a basc nformaton and control flow nto the reference model. The nformaton flow s responsble to mantan the nformaton status of the dfferent decson-makng enttes by takng feedback from the base system and process and other decson-makng enttes nto account, whle the control flow models how plannng and control nstructons are communcated n the supply network. 7. Fnally, the reference model has to be represented n such a way that the dfferent end-users can easly use the smulaton reference model wthout a specfc smulaton engne. ASCII fles or XML data structures are approprate to represent the model. The representaton to be offered has to take the relatonshp between the dfferent enttes as ndcated n Fgure 1 nto account. 8. A documentaton of characterstc performance measures for the dfferent nodes of the supply network has to be provded for end-users to check the correctness of ther usage of the reference model wth the default settngs. Fgure 1 contans an Entty-Relatonshp Model (ERM) for mportant enttes of a supply network n the semconductor ndustry. Order_ID places n Customer Order Order Entry Date Confrmed Date Quantty Product_ID 1 Order n contans 1 Product Customer_ID Locaton Customer Replenshment Order n Delvery Date Begn End m contans / processes n places 1 Internal Transt Tme m External Transt External Transt Tme n Producton Ste Storage Locaton Internal Transt n Faclty_ID m Faclty Locaton Fgure 1: Important enttes n a semconductor supply chan We contnue wth a descrpton of mportant enttes of the reference model. 3.2 Base System We start wth the base system. It represents, together wth the base process, the materal flow n the supply network. It contans the followng enttes: 2127

two front-end facltes (FE) two slcon foundres (SF) two de banks (DB) two back-end facltes (assembly and test (AT)) two subcontractors (SC) two (regonal) dstrbuton centers (DC). We decde to avod a dfferentaton between wafer fabs and sort to have a low level of detal. At the same tme, we do not model assembly and test usng dfferent models. Such fne-graned models are not dscussed n most of the projects to create a smulaton model for supply chans n the semconductor ndustry (Goddng, Sarjoughan, and Kempf 2003; Lendermann et al. 2003; Chong et al. 2006; Huang et al. 2009). Outsourcng optons are modeled by SFs and SCs. The DBs are used to decouple front-end and back-end, whereas the DCs are between the AT facltes and the customers. Of course, customers are also part of the base system. However, we consder them as external enttes that are dscussed n Subsecton 3.3. Supplers of raw materal are not modeled n the reference model because they are rarely a bottleneck n the semconductor ndustry. We wll explan the representaton of capactes n the base system n more detal n Secton 4. Next, we have to nclude products nto the reference model because they represent the base process. The reference model contans the followng enttes: two fnal products A and B wth two stock keepng unts per product two addtonal fnal products C and D of the same product famly that have the same parent des produced n the frond-end and packaged dfferently n the back-end, only one stock keepng unt s assgned to each of the fnal product C and D. For each product we specfy n whch front-end and back-end facltes they can be processed. Furthermore, emprcal cycle tme dstrbutons that depend on the load of the correspondng faclty are provded for each product. We also specfy a smple transportaton model that ncludes nternal and external transt tmes. In contrast to the cycle tme, transt tmes are assumed to be determnstc. 3.3 External Enttes Customers have to be ncluded nto the reference model because they provde orders and more generally, demand. The followng enttes are used: one customer that s an orgnal equpment manufacturer (OEM) one customer that s a dstrbutor. The two non-dversfed fnal products A and B descrbed n Subsecton 3.2 are assgned to the two customers. The fnal products C and D are assgned to the dstrbutor. We assume weekly tme buckets for 18 months to take the typcal lfecycle of products n the semconductor ndustry nto account. The reference model contans the followng order and demand patterns for the four products from Subsecton 3.2: frm orders supply reservaton fnal demand. Frm orders are customer orders that have already been confrmed by the Order Management system. It s a bndng demand that s cancellable under certan condtons. The amount of frm orders s decreasng over the tme horzon. Frm orders have a default due date n the reference model. Supply reservaton s an addtonal forecasted demand that comes ether from the Sales & Operatons Plannng process or from the customer through a Busness to Busness (B2B) Electronc Data Interface (EDI). A customer forecast s a non-bndng demand that can be cancelled wthout any restrcton. It s used as a placeholder for frm orders that may arrve at a future pont of tme. The amount of supply reservatons s ncreasng over the plannng horzon. There s no supply reservaton for the frst perod. The fnal demand states the defntve 2128

expectaton of the customer for a gven tme bucket. It s relevant for demand fulfllment and s equal to the frm orders of the frst tme bucket. We start by generatng fnal demand for each fnal product for a gven demand level. It s assumed that the demand for the fnal products s ndependent. Some random nose wth respect to the dfferent quanttes s taken nto account. Based on the fnal demand, frm orders and supply reservaton are determned. Forecast errors are taken nto account by modelng the error as a normal dstrbuted random varable wth a prescrbed mean and standard devaton. Some random nose s modeled n a smlar way for the frm orders to model cancellatons. Note that n addton the fnal demand, frm orders, and supply reservaton can be based on real-world data from Infneon. A statonary demand scenaro and a scenaro that contans some rampng and drop down of the four products are provded n the reference model. 3.4 Plannng System and Control System In the sprt of the MIMAC models that do not provde dspatchng rules for producton control, we do not nclude any sophstcated plannng and control logc n the reference model. We assume an enterprsewde plannng and control unt that provdes nstructons for the dfferent FE and AT facltes. The dfferent FE facltes do perform only short-term producton plannng actvtes. We smply take the fnal demand and determne lot release schedules for each tme bucket by a smple backward calculaton scheme takng target cycle tmes nto account. Note that the producton of parent des for product C and D can be planned n an aggregated manner. Then the release quanttes are assgned to the FE facltes and SFs usng statc allocaton rules. Approprate lot szes and lots have to be determned for FE and AT facltes, respectvely. Default lot szes for AT facltes are specfed n the reference model. Decsons wth respect to the safety stocks for DBs and DCs are also made by the plannng system. The default settng s zero safety stock. Important performance measure values are documented n the reference model. The smple plannng logc can be replaced by a more sophstcated end-user specfc one. We wll see, n Secton 4, that a detaled smulaton model of a supply network s also proposed that conssts of detaled smulaton models for FE and AT facltes. In ths stuaton, performance measure values are only reported for a Frst-In- Frst-Out (FIFO) producton control strategy. 3.5 Modelng of Informaton and Control Flows Some smple models of the nformaton and control flow have to be ncorporated nto the reference model. The frm orders are generated weekly and are sent to the enterprse-wde plannng and control unt. Ths unt receves feedback from the AT facltes and the SCs,.e., nformaton on completed lots. The DCs report ther shpments to the plannng and control unt. Each FE faclty and each SF nforms the plannng and control unt when wafers are completed. Informaton related to DB and DC nventores s transferred to the FE and AT facltes n case of assemble-to-order and make-to-stock manufacturng strateges, respectvely. Lot release schedules are sent to the FE facltes n case of a make-to-order strategy. Fnally, the SFs and SCs obtan nstructons related to quanttes for dfferent products to produce them. The descrbed settng s depcted n Fgure 2. 4 SIMULATION MODELING OF THE BASE SYSTEM AND PROCESS We start by descrbng our approach to model sngle nodes n a semconductor supply chan. Then, we extend ths approach to the network stuaton. We descrbe the structure of the proposed reference models. 4.1 Modelng of Sngle Manufacturng Nodes Two dfferent approaches are used to model sngle manufacturng nodes. The frst approach conssts n consderng full models of FE facltes and AT facltes. For FE facltes, we use the MIMAC-I model (MASM 1997). It conssts of 73 tool groups. The products have around 240 processng steps. It contans 2129

batch processng tools and reentrant process flows. Operators are not modeled. Moreover, we also consder a smulaton model of an AT faclty. It conssts of 23 tool groups. Some tool groups nclude sgnfcant sequence-dependent setup tmes. Lots are splt, for example, n front of the wre-bonder tool group. Ths model s called Back-end-I. The smulaton AutoSched AP s used as smulator. We refer to these models as academc models n the remander of ths paper. However, usng full smulaton models leads to sgnfcant large smulaton tmes and large effort to mantan the smulaton models. orders Enterprse-wde Plannng/Control Unt DB nventory/ release quanttes wafers completed DB nventory completed lots FE AT shpments/dc nventory Plannng/Control Plannng/Control raw wafers Base System DB Base System DC ICs OEM...... DB SF SC Dstrbutor raw wafers DC ICs materal flow nformaton/control flow Fgure 2: Flow-orented vew on the supply network To avod these problems, we use a model reducton approach that s nspred by Duarte et al. (2007). Our approach takes nto account that cycle tmes are load-dependent. Therefore, we consder for a gven FE or AT faclty dfferent load levels L, 1,..., n. A specfc load level s determned by the number of released lots per tme unt and leads to a certan bottleneck utlzaton. We assume that the bottleneck utlzaton for L s smaller than for L 1. We determne an emprcal dstrbuton of the cycle tmes and an emprcal dstrbuton of the tme elapsed between two consecutve lot completons for a gven L. The correspondng emprcal dstrbutons are called CT and TP. Note that TP represents the throughput. Consequently, each L s represented by a par TP, CT. We release lots accordng to TP by determnng the correspondng nter arrval tmes. Each released lot obtans an ndvdual cycle tme accordng to a realzaton of the emprcal dstrbuton. Note that the pars TP, CT can be easly determned usng full smulaton models,.e., n case of the academc models, or real-world data. Usually three to fve pars,.e., load levels, are enough. The procedure s shown n Fgure 3. 2130

nter arrval tme accordng to TP cycle tme accordng to CT, no further delay lot release processng completed lots Fgure 3: Basc prncple of the reduced smulaton model Now we consder the stuaton where a load level L j s of nterest whch leads to a bottleneck utlzaton that s between the utlzaton caused by L and L 1. Smlar to the lnearzaton approach for clearng functons proposed by Asmundsson, Rardn, and Uzsoy (2006), we nterpolate lnearly between CT and CT 1 as follows E1 E j E j E CT j CT CT1, (1) E E E E 1 where we denote by CT k a realzaton of the cycle tme for L k and by E k the mean nter arrval tme for L k. The man dfference of our approach to the approach by Duarte et al. (2007) s ths lnear nterpolaton. Duarte et al. (2007) determnes an emprcal cycle tme dstrbuton for a low load stuaton and an emprcal throughput dstrbuton for a hgh load. A correcton term for the cycle tme s calculated usng Markov chans. However, t cannot be assumed that ths data s always avalable n real-world nformaton systems. The approach proposed n ths paper has been successfully assessed usng the academc models and also real-world data from one of Infneon s FE and AT facltes, respectvely. 4.2 Modelng of an Entre Manufacturng Network Because we are nterested n smulaton models of supply networks, we create such models usng the models for sngle nodes descrbed n Subsecton 4.1. In a frst step, we buld a smulaton model of a supply network that contans two MIMAC-I FE facltes and two Back-end-I facltes. Furthermore, the full smulaton model also contans two DB. Each FE lot s splt nto three AT lots. Ths full model s the base of our frst reference model. Next, we buld a reduced varant of ths model usng the reducton technque outlned n Subsecton 4.1. Two products are consdered for two dfferent load stuatons L 1 and L 2 for the FE facltes. Because the lot release rates n the FE facltes have an mpact on the load of the AT facltes, we nclude DBs between the two stages to decouple them. A target Work-In-Progress (WIP) level s determned for the AT facltes based on Lttle s law and the target cycle tme and target throughput for the AT facltes. We nterpolate between the two dfferent load stuatons to obtan a thrd load stuaton L 12. Fgure 4 depcts the resultng cycle tme hstograms for the two products and L 2 and L 12, respectvely. It can be seen from Fgure 4 that the cycle tme hstogram for the full and reduced models are rather smlar n case of product p_2. The smulaton s run for 1000 days. Ths smlarty s smaller for product p_1, however, the shape s stll the same. 1 2131

30% Load level L 2 (p_1) 30% Load level L 12 (p_1) 25% 25% Frequency 20% 15% 10% Frequency 20% 15% 10% 5% 5% 0% 15 24 33 42 51 60 CT n days 0% 15 24 33 42 51 60 CT n days Full supply network model Reduced model Full supply network model Reduced model 30% Load level L 2 (p_2) 30% Load level L 12 (p_2) 25% 25% Frequency 20% 15% 10% Frequency 20% 15% 10% 5% 5% 0% 15 24 33 42 51 60 CT n days 0% 15 24 33 42 51 60 CT n days Full supply network model Reduced model Full supply network model Reduced model Fgure 4: Comparson of a full supply network model and a reduced model We ntroduce the measure H 2 d H 1 d dd H 1 d H 2 d to determne the smlarty between two hstograms H 1 and 2 by H the frequency of the category d. We obtan D 0. 2607 d H D (2) abs dd H n a more formal way. Here, we denote H abs for p_1 and HD abs 0. 1701 for p_2. Statstcs for the correspondng cycle tme dstrbutons are summarzed n Table 1. We can see that the varance s lower n the reduced model due to the lnear nterpolaton accordng to expresson (1). Table 1: Statstcs for cycle tme dstrbuton obtaned by the full and reduced smulaton models Compare Product p_1 Product p_2 full model reduced model full model reduced model mnmum 12.917 16.756 18.139 21.839 maxmum 44.559 40.217 61.022 55.775 medan 27.324 27.149 33.791 35.044 mean 27.508 27.314 34.425 35.429 varance 35.418 13.469 63.404 37.289 coeffcent of varaton 0.216 0.134 0.231 0.172 A smlar supply reference model wll be provded that s based on real-world data from Infneon. It ncludes two FE facltes and two AT facltes as descrbed n Subsecton 3.2. 2132

5 POTENTIAL APPLICATION SCENARIOS The frst applcaton scenaro s related to the assessment of master plannng approaches for supply networks n semconductor manufacturng. Here, for gven frm orders and supply reservaton, producton quanttes for each FE and AT faclty of the supply network are determned. The plannng approaches are used wthn a rollng horzon settng. Smulaton can be appled to represent the base system and the base process. The performance of the proposed approaches can be assessed by approprate robustness measures usng a stochastc demand and a stochastc base system. Heurstcs for a smplfed master plannng problem ncludng several FE facltes and the correspondng smulaton-based performance assessment are dscussed by Ponsgnon and Mönch (2010). The approach by Hung and Leachman (1999) s appled to reduce the smulaton models of the full FE facltes. When the framework s used, the smple plannng logc descrbed n Subsecton 3.4 has to be replaced by a more sophstcated master plannng approach. Generally, long and medum term plannng approaches for entre semconductor networks are rarely dscussed n the lterature. One reason for ths stuaton mght be the huge modelng effort to create homegrown smulaton models from scratch to apply these approaches n a rollng horzon settng. The second applcaton scenaro deals wth make-to-stock, assemble-to-order, and make-to-order decsons (see Sun et al. 2010 for a related study). Here, the default due dates of orders have to be changed n the reference models to model the fact that, for make-to-stock and assemble-to-order, fnal products can have a tght due date, and approprate safety stocks have to be added to the reference model to verfy the effect on delvery relablty to the customers. Note that the two dscussed applcaton scenaros can be based on the reference models that do not nclude full smulaton models of the FE and AT facltes because producton control decsons,.e., dspatchng or schedulng decsons for lots on tools, are not mportant. Hence, the fast smulaton of the reduced models offers some advantage. Usng the full supply network reference model makes sense when the nteracton between the producton plannng and producton control strategy should be studed n detal. 6 CONCLUSIONS AND FUTURE RESEARCH In ths paper, we dscussed mportant steps towards the ultmate goal of havng reference models for supply chans n the semconductor ndustry. After determnng requrements for smulaton reference models, we dentfed the man buldng blocks of such models. Then, we presented an approach to obtan a supply chan network as a set of reduced smulaton models usng an approach smlar to the abstracton method proposed by Duarte et al. (2007). We demonstrated that ths approach works well for both academc models and models based on data from the ndustry, respectvely. Fnally, we dscussed some potental applcatons scenaros for the reference models. There are some drectons for future research. Frst of all, we have to complete the proposed smulaton reference models by addng more detals wth respect to modelng customers,.e., order generaton, forecast, product ramp-ups and drop-downs, and by modelng nventory ponts. Furthermore, more research s needed to adjust the models to dfferent product mx stuatons because so far we assumed only very smple stuatons wth a small number of products. Fnally, t s hghly desrable to demonstrate the mpact of the reference models by usng them for the applcaton scenaros descrbed n Secton 5. ACKNOWLEDGMENTS We would lke to thank Andreas Klemmt who helped us wth a smulaton model for AT facltes and some useful dscusson on modelng ssues for such facltes. 2133

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ulaton Conference, edted by B. Johansson, S. Jan, J. Montoya-Torres, J. Hugan, and E. Yücesan, 341-349. Pscataway, New Jersey: Insttute of Electrcal and Electroncs Engneers, Inc. Schunk, D., and B. Plott. 2000. Usng Smulaton to Analyze Supply Chans. In Proceedngs of the 2000 Wnter Smulaton Conference, edted by J. A. Jones, R. R. Barton, K. Kang, and P. A. Fshwck, 1095-1100. Pscataway, New Jersey: Insttute of Electrcal and Electroncs Engneers, Inc. Sper, J., and K. G. Kempf. 1996. Smulaton of Emergent Behavor n Manufacturng Systems. In Proceedngs of the IEEE/SEMI Advanced Semconductor Manufacturng Conference, 90-94. Sun, Y., D. L. Shunk, J. W. Fowler, and E. S. Gel. 2010. Strategc Factor-Drven Supply Chan Desgn for Semconductors. Calforna Journal of Operatons Management 8(1):31-43. AUTHOR BIOGRAPHIES HANS EHM s Prncpal of Logstcs Systems of Infneon Technologes AG. He holds degrees n Physcs from Germany and a M.S./OSU. In over 20 years n the Semconductor ndustry, he was granted managng and consultng postons at Wafer Fabrcaton, at Assembly & Test and nowadays for the global Supply Chans - on producton ste, on busness unt and on corporate level. He s Board member of cam- Lne Holdng AG, an IT company for supply- and qualty chans. He led Governmental supported projects on natonal and nternatonal level n the context of IT, Semconductor Manufacturng and Supply Chans - wthn such projects he ntated the EU/US MIMAC co-operaton on capacty modelng. He s a member of INFORMS. Hs emal address s hans.ehm@nfneon.com. HANNA WENKE s a PhD student and research and teachng assstant n the Department of Mathematcs and Computer Scence at the Unversty of Hagen, Germany. She receved a master s degree n appled mathematcs from the Unversty of Münster, Germany. Her current research nterests are n schedulng and smulaton n manufacturng, logstcs, and servce operatons. Her emal address s Hanna.Wenke@fernun-hagen.de. LARS MÖNCH s Professor n the Department of Mathematcs and Computer Scence at the Unversty of Hagen, Germany. He receved a master s degree n appled mathematcs and a Ph.D. n the same subject from the Unversty of Göttngen, Germany. Hs current research nterests are n smulaton-based producton control of semconductor wafer fabrcaton facltes, appled optmzaton and artfcal ntellgence applcatons n manufacturng, logstcs, and servce operatons. He s a member of GI (German Chapter of the ACM), GOR (German Operatons Research Socety), SCS, INFORMS, and IIE. Hs emal address s Lars.Moench@fernun-hagen.de. THOMAS PONSIGNON s a Ph.D. canddate n the Department of Mathematcs and Computer Scence at the Unversty of Hagen, Germany. He s also workng at Infneon Technologes AG n the feld of supply chan management. He receved master s degrees n ndustral engneerng from the EPF-Ecole d Ingéneurs, Sceaux, France and the Unversty of Appled Scences, Munch, Germany. Hs research nterests nclude producton plannng and smulaton for semconductor manufacturng networks. Hs emal address s Thomas.Ponsgnon@nfneon.com. LISA FORSTNER s a Ph.D. canddate n the Department of Mathematcs and Computer Scence at the Unversty of Hagen, Germany. She s also workng as a PhD student at Infneon Technologes AG n the feld of supply chan management. She receved master s degrees n ndustral engneerng from the EPF- Ecole d Ingéneurs, Sceaux, France and the Unversty of Appled Scences, Munch, Germany. Her research nterests nclude nventory management, make-to-order/make-to-stock decsons, and smulaton for semconductor manufacturng networks. Her emal address s Lsa.Forstner@nfneon.com. 2135