INSTITUTE OF PHYSICS PUBLISHING JOURNAL OF MICROMECHANICS AND MICROENGINEERING

J. Micromech. Microeng. 13 (2003) 509–522 PII: S0960-1317(03)36842-1

Manufacturing process and materialselection in concurrent collaborativedesign of MEMS devicesXuan F Zha1,3 and H Du2

1 Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 6380752 School of Mechanical and Production Engineering, Nanyang Technological University,50 Nanyang Avenue, Singapore 639798

E-mail: zha@cme.nist.gov

Received 10 May 2002, in final form 31 March 2003Published 14 May 2003Online at stacks.iop.org/JMM/13/509

AbstractIn this paper we present knowledge of an intensive approach and system forselecting suitable manufacturing processes and materials formicroelectromechanical systems (MEMS) devices in concurrentcollaborative design environment. In the paper, fundamental issues onMEMS manufacturing process and material selection such as concurrentdesign framework, manufacturing process and material hierarchies, andselection strategy are first addressed. Then, a fuzzy decision support schemefor a multi-criteria decision-making problem is proposed for estimating,ranking and selecting possible manufacturing processes, materials and theircombinations. A Web-based prototype advisory system for the MEMSmanufacturing process and material selection, WebMEMS-MASS, isdeveloped based on the client–knowledge server architecture and frameworkto help the designer find good processes and materials for MEMS devices.The system, as one of the important parts of an advanced simulation andmodeling tool for MEMS design, is a concept level process and materialselection tool, which can be used as a standalone application or a Java appletvia the Web. The running sessions of the system are inter-linked withwebpages of tutorials and reference pages to explain the facets, fabricationprocesses and material choices, and calculations and reasoning in selectionare performed using process capability and material property data from aremote Web-based database and interactive knowledge base that can bemaintained and updated via the Internet. The use of the developed systemincluding operation scenario, use support, and integration with an MEMScollaborative design system is presented. Finally, an illustration example isprovided.

(Some figures in this article are in colour only in the electronic version)

1. Introduction

It is now widely accepted that the final cost of a manufacturedproduct is largely determined at the design stage. Designers

3 Present address: Manufacturing System Integration Division, NationalInstitute of Standards and Technology, Mail Stop 8263, 100 Bureau Drive,Gaithersburg, MD 20899, USA.

will tend to conceive parts in terms of processes and materialswith which they are familiar and may, as a consequence, notconsider process and material combinations that may haveproven more economic [1, 3, 13]. ‘Microelectromechanicalsystems (MEMS) device design must be separated fromthe complexities of the fabrication sequence and packagingprocesses with consideration of different materials andprocesses’ [23, 28]. A MEMS device designer requires a high

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level of fabrication and packaging knowledge in order toembody a successful design. Furthermore, the developmentof even the most common MEMS device requires dedicatedwork to formulate a suitable fabrication sequence andpackaging process. MEMS and microelectronic deviceperformance analysis is rendered virtually useless if thematerial information used in the model is wrong. The errorassociated with the final simulation results will correlateone-to-one with the error associated with material propertyestimation.

However, most MEMS devices are currently modeledusing weak analytical tools, resulting in a relatively inaccurateprediction of performance behaviors [23]. The MEMS designprocess is usually performed in a trial-and-error fashion,which requires several iterations before the performancerequirements of a given device are finally satisfied. Thisnon-ideal design methodology combined with the length oftime and high costs associated with MEMS prototyping resultsin a very inefficient and ineffective scenario for commercialproduct development. With the development of MEMS,advanced simulation and modeling tools for MEMS design areurgently needed. The design and manufacturing (fabricationand packaging) of MEMS and microelectronic devices andsystems need to improve considerably from their currentprimitive state [23]. The advanced simulation and modelingtools for MEMS design must provide an advisory service soas to help the designer select manufacturing processes andmaterials for MEMS devices [9, 29, 32–37]. It is thereforesignificant to develop an efficient method and system ina computer-aided concurrent collaborative environment fordesigners to use at the early stages of MEMS design.

With the advent of wide-area networks and the Internet-based World Wide Web (WWW), it is believed that manyof the largest beneficiaries of MEMS technology willbe firms that have no capability or core competency inmicrofabrication technology and access by these companiesis critically important to the successful utilization ofMEMS fabrication facilities. A mechanism or frameworkallowing these organizations to have responsive and affordableaccess to MEMS fabrication resources for prototyping andmanufacturing is essential [23]. The advisory service systemcan be used to help communicate the abilities of new processesto designers using the Internet and Web, as advocated firstby Frost and Cutkosky [14]. The availability of suitabledesign tools combined with computer networks to provideaccess to high performance workstations and local or remotesupercomputer capability can radically alter this situation.The Internet-based WWW enables developers to provideintelligent knowledge services [10, 33–37]. Expert systemsrunning on servers can support a large group of users whocommunicate with the system over the network, in whichuser interfaces based on Web protocols provide access to theknowledge servers for services. Expert system technologywould lead to the development of many small and medium-sized advisory systems that could help many categories ofnovice users in performing expert-level tasks. Therefore,it provides an opportunity for making the manufacturingadvisory expert system widely available via the Web.

The aims of this paper are to develop a knowledge-basedmethod and a Web-based advisory system for selecting suitable

processes and materials for MEMS devices in concurrentcollaborative design environment:

(1) to explore a new knowledge intensive intelligentmethodology for estimating and ranking manufacturingprocesses/materials;

(2) to develop a new client–knowledge server architecture andframework for manufacturing process/material selection;

(3) to develop a prototype advisory system for manufacturingprocess/material selection using Java and CORBA overthe Internet and WWW;

(4) to integrate the developed manufacturing advisory systeminto a self-developed MEMS design system.

The system is especially dedicated for generating process andmaterial selection advice during the embodiment design ofmicro-machined components.

The remaining parts of this paper are organized asfollows. We begin with a review of the existing MEMS designsystems for process and material selection. Then we discussconcurrent design framework, manufacturing process andmaterial hierarchies for MEMS, selection strategy, method,and implementation of such a strategy and fuzzy decsionsupport method including the development of a Web-basedprototype advisory service system and its use. Finally wesummarize some conclusions and observations.

2. Current status of research

In this section, the previous and current work relatedto manufacturing process and material selection is brieflyreviewed. The focus is on an overview of the functionalityof the current MEMS design systems related to manufacturingprocess and material selection for MEMS devices.

Generally, the selection of a suitable process–material pairto manufacture a component or device is not a straightforwardmatter [5, 6, 12, 30]. There are many factors which needto be considered at the design stage, for example, the sizeof component, the material to be processed and toleranceon dimensions. While all processes have slightly differentcapabilities, there is also a large overlap; for many componentsthere are a large number of processes which can be used.Methods and software tools for process selection stem fromthe more widespread use of computer tools to assist withmaterial selection. Some popular commercial tools aredocumented for material selection [1]. Process selectiontools are more rare than the material selection tools suchas Computer-aided Material and Process Selection (CAMPS,[2]), Design Advisor (DA, [38]), Material and ManufacturingProcess Selection (MAMPS, [15, 16]), Cambridge ProcessSelector (CPS, [12]), Process Sequencing Expert Shell (PSES,[13]), and Manufacturing Advisory System (MAS 1.0 &2.0, [4, 7, 30]). These systems are mainly dedicated tocommon (macro level) product manufacturing process andmaterial selection. Details can be found in [30, 34]. Fora micro level product (i.e. MEMS) manufacturing processand material selection, no such system is yet availablealthough there are several commercial MEMS design systemssuch as MEMCADTM (now COVENTORWARETM) [8],IntelliSuiteTM [18], MEMSCAPTM [22], etc, which supportintegrated modeling and process. These systems have some

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functionality for MEMS manufacturing process and materialselection or contain some embedded modules for this purpose.In what follows, we give an overview of the functionality of theabove three MEMS design systems in manufacturing processand material selection for MEMS devices.

DESIGNERTM is Coventor’s powerful front-end designtool for creating layouts and three-dimensional (3D) modelsof manufacturable MEMS devices. From DESIGNERTM

users/designers can create models for further analysis orexport files for mask making and fabrication. Manufacturingprocesses and material properties can be added to create 3Dmodels. The process emulator in it can emulate fabricationsteps for MEMS devices and use standard processes ofdeposition and etching with control of bulk and thin-filmgeometries and materials. The material properties databaseis used for proper identification of each process layer in theMEMS device. The layout geometry and fabrication processinformation are furnished for automatic 3D model generationand visualization.

IntelliSuiteTM takes a process-oriented approach toMEMS design and analysis, which starts the design process notfrom device geometry but from fabrication machine settings.Incorporating process templates, material data, mask layout,and device analysis, it provides a platform for the entire designteam to develop manufacturable devices. It is the only CAD forMEMSTM tool to address process parameters linked with thin-film material properties. Processes can be custom-designedone step at a time, or designers/engineers can draw froma wide range of foundry-ready process templates. Userscan also create their own process flows from a database ofover 70 process steps or by including custom process steps.IntelliSuite’s process and materials databases have becomea major MEMS resource. MEMaterialTM, as a mostcomprehensive thin-film materials database with over 70materials included, can provide a vital link between the processparameters and the device behaviors. It allows users to predictmechanical, electrical, thermal, physical, optical, and othermaterial properties as a function of processing parameters. Asthe database is extensive, based on measured data rather thanconstitutive relationships, users can expand with their ownproprietary data.

MEMSCAP offers two primary software MEMS designtool suites: MEMS XplorerTM, for UNIX workstations andMEMS ProTM, for PC operating systems. The two CADsoftware tools provide a system-level approach, enablingdesigners to develop new MEMS designs, integrate existingdesigns (intellectual property or IP), and couple them withthe system electronics that will drive them. The tool suitesoffer a comprehensive and customizable design environmentfor the development and testing of MEMS-based products.MEMSCAP CAD tools are open-platform products thatsupport leading electronic design automation environmentsused for integrated circuit (IC) development. These toolsallow data sharing between system designers, IC designers,process engineers and MEMS experts, permitting earlier andconsistent design checks between multidisciplinary teams.MEMSCAP also provides modular subsets of these toolsfor those customers not requiring full capability. Each ofthe tool suites has easy-to-use graphical interfaces for rapiddesign. MUMPStartTM is an all-in-one MEMS design kit.

MEMS Pro’s built-in Technology Manager permits targetingof specific process technologies.

From the above overview of the current MEMS designsystems, their functionality for MEMS manufacturing processand material selection can eliminate a large amount ofwork during design, analysis, and simulation such as tablelookup, data sharing, form filling, and process/materialcoding. However, they may still suffer one or more of thefollowing drawbacks: a heavy dependence on the experienceand knowledge of designers/users; no built-in ways forselection or decision-making knowledge representation; nomechanisms to utilize the given knowledge and guide thedesigner/user; difficult to modify, not easy to extend or update,and no mechanism to provide advisory services or explain theresults and what-ifs. Also, they are generally specialized andstandalone applications, and still lack effective and efficientmethods for selecting manufacturing processes and materialsand their combinations. It is very difficult for designersto use them for understanding and designing the integrateddistributed performances of product systems when selectingdifferent materials and processes. Thus, they are unable tosupport and coordinate highly distributed and decentralizedcollaborative MEMS design and modeling activities. Toovercome the above drawbacks and improve the currentmethod of selecting manufacturing processes and materialsand their combinations, this research develops a knowledge-intensive decision support method and a Web-based advisorysystem to help designers/users collaborate and make rapidand more intelligent decisions in selecting manufacturingprocesses and materials for MEMS devices. The motivationand vision presented in this paper share some themes withthe above macro-level process and material selection softwaresystem, especially MAS 2.0 [30] but focus on MEMS (micro-level), providing not only the choice but also advisory service.Details are discussed below.

3. MEMS design and manufacturing integration:concurrent design framework

MEMS is a hybrid of mechanical elements, sensors, actuators,and electronics on a common silicon substrate through theutilization of microfabrication technology. It promises torevolutionize nearly every product category by bringingtogether silicon-based microelectronics with micromachiningtechnology, thereby making possible the realization ofcomplete ‘systems-on-a-chip’ [18]. In this section, we discussissues of MEMS design and manufacturing and how to selectmaterials and manufacturing processes.

3.1. Concurrent MEMS design framework

As stated above, one of the challenges of MEMS technologyis that MEMS devices or systems design must be separatedfrom the complexities of the fabrication sequence andpackaging processes with consideration of different materialsand processes. In our previous work [32–37], a knowledge-intensive methodology for design and simulation of MEMSdevices was developed, and a Web-based knowledgeintensive support framework was built up to supportconcurrent collaborative design of MEMS. In this concurrent

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(a) (c)

(b)

Figure 1. Hierarchies of MEMS manufacturing processes (a), (b) and their implementation (c).

collaborative design framework, an interface is designedto separate design from fabrication/packaging processes,which allows the designer to use process-independent designtools and methodologies [23, 28]. It will enable moremanufacturable designs, correct the first time or with feweriterations, to become routine so that the amount of time andeffort required to realize MEMS devices can be reduced.Since extensive knowledge of fabrication is no longer be aprerequisite before starting design activities, more designersare able to participate in design activities, and this will resultin increased levels of innovation and creativity. Furthermore,the interface separating design from fabrication enables higherlevels of integration without increasing development time orcosts. To be of most utility, the interface allows designers tohave ability and know the manufacturing implications of theirdesigns at design time, and fabrication specialists can providethe needed functions to aid designers.

3.2. MEMS manufacturing process hierarchy

While the electronics are fabricated using IC processsequences, the micromechanical components are fabricatedusing compatible ‘micromachining’ processes that selectively

etch away parts of the silicon wafer or add new structural layersto form the mechanical and/or electromechanical devices.

To divide the universe of all-possible semiconductorfabrication processes into increasingly specialized domains,a process hierarchy, as shown in figure 1, is established intowhich all-possible steps in the fabrication of MEMS devices,including metrology and testing, will fit [23]. This kindof hierarchical process structure can be used to visualizeand organize the process capability database containinginformation about the component processes, materials, andvendors, etc. It can bring two major benefits: (1) to helpfamiliarize newcomers to semiconductor fabrication with thetechnology and terminology; (2) to give experienced users areference point to describe the scope of (potentially) availableprocesses and the terminology for description.

As shown in figure 1, the top level of the hierarchy includesdeposition,etch,and pattern transfer,mask making, and otherbroad, high-level terms. Descending a layer,thedepositionsub-divides into chemical vapor deposition (CVD), epitaxy,andphysical deposition; the other high-level nodes in thehierarchy (or generic processes) divide similarly. As such,the designers or manufacturers allow expanding branches ofthe hierarchy and view a set of actual processes under

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Screening apply limits

(Eliminating, ranking, evaluating and selecting)

All materials & processes

Subsets of processes or materials

Supporting information & knowledge

(Database & Knowledge base)

Shortlist of candidates

Local conditions or requirements or constraints

Final material and process choice

Figure 2. The strategy for process and material selection.

each branch. Through the open Web database of processcapabilities below, users/designers can search along differentdimensions such as material deposited or etched. Thus,designers/users can also select processes from the database ofprocess capabilities to construct a process sequence through aprocess sequence builder.

3.3. MEMS manufacturing process and material selection

The selection of a suitable process–material pair tomanufacture a MEMS component or device is not astraightforward matter. When selecting MEMS manufacturingprocesses and materials, a reasonable number of possiblealternatives are available. The procedure of selectingmanufacturing processes and materials for MEMS devices isto examine the alternatives against econo-technical criteria. Itis actually a multi-criteria decision-making problem. Thus,the problem of MEMS manufacturing process and materialselection can be defined as: given a set of alternatives, evaluateand select an alternative that can satisfy customer needs, meetdesign requirements and fit the technical capabilities of acompany. Figure 2 shows a general strategy for process andmaterial selection.

Accounting for the complexity of manufacturing processand selection, a variety of methods should be used to rank theappropriateness of an option with the value of a requirement.In this research, a comprehensive knowledge-intensive methodis adopted, which is constructed using various decision-making methods to rank the options with requirement valuesand support knowledge. Due to the uncertainty and fuzzinessof design specifications and technical requirements in the earlyconceptual design stage, it is difficult to assess the process

Merit Figure

Requirement Rank

Weighting Function

77 100 50

Fuzzy Table Lookup Integer

5 Parts Copper Days RequirementValues

Rank Method

Option Rank

Figure 3. Rank methods.

and material performance in this stage. The kernel of theknowledge intensive method is a fuzzy ranking algorithmfor multi-criteria decision-making problems. Details arediscussed in the next section.

4. Knowledge support scheme for manufacturingprocess and material selection

In this section, we describe details about ranking methods and aknowledge decision support scheme for manufacturing processand material selection. The focus is on the fuzzy rankingalgorithm for fuzzy multi-criteria decision-making problems.

4.1. Ranking methods and knowledge support scheme

The method used in this research is based on rankingtechniques [27, 20, 21] for evaluating and selecting possibleprocess/material combinations for a particular component partin terms of total part costs. The basic ranking scheme isdescribed as follows. The user enters design specifications forone or more requirements (R). Each possible process/materialcan then be assigned a requirement rank, Rreq, based upon therequirement’s value, Vreq. To obtain the ranking for a process,each of its requirement ranks contributes to a weightingfunction. The output of this weighting function, the figureof merit, is used as the final option rank, RO. The systemrepeats this for every option (O) each time a requirementvalue is changed. Figure 3 illustrates this process. Eachrequirement has a method for calculating the rank (R). Themethod parameters are process-dependent. A brief descriptionof the methods used in obtaining the requirement ranks hasbeen discussed in [30]:

(1) Trapezoidal fuzzy numbers. This generates a rank for arequirement based upon the design specification anda trapezoidal membership function. This is actuallya simplified mode from the fuzzy rank method (seesection 4.2).

(2) Boolean list membership. The option has a list of thingsit cando. Anything not on the list is impossiblefor theprocess to do.

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Eliminate

Unacceptable

Alternatives

Feasible Set Base Set

Evaluate

Candidates &

Customization

Final choice

Requirements & Constraints Secondary Requirements & Constraints

Make

Decision

Knowledge Decision Support System

Knowledge Source Required

• Differentiating Features

• Preferences & Importance

(Weights)

• Trade-offs

• Utility Functions

• Heuristic Rules

•……

Ranks

Figure 4. Knowledge support scenario for process and material selection.

(3) Table lookup. For the more complex case of determiningmaterial/manufacturing process compatibility, a two-dimensional array is used to look up a compatibility factor.

(4) Integer programming. For qualitative requirements thathave value ranges or orders of magnitude, such asproduction setup times ‘hours’ or ‘months’. A singleinteger is used to represent the requirement’s value.

The key elements of a process and materialselection tool are composed of database and decisionsupport systems/modules. The database support systemcommunicates to the user with an extensive, up-to-date set ofalternatives, while the decision support system concerns itselfwith evaluation, comparison and selection of alternatives. Thedecision support system consists of a multi-layered explorationand is knowledge-based. Figure 4 depicts a knowledge supportscenario of process and material selection. The user may entera bare minimum of data, just the batch size or the needed lineartolerance, and obtain initial feedback about the appropriatenessof various manufacturing options [30]. However, should thedesigner wish to provide more information, they may fillin more requirements. Many of the requirements have anadvanced mode to allow the users to more explicitly define theirrequirements. The kernel of the knowledge-based decisionsupport scheme is a fuzzy ranking algorithm for multi-criteriadecision-making problems. Details about the fuzzy rankingmethods are discussed below.

4.2. Fuzzy ranking method

One of the well-known methods for multi-criteria decision-making is the procedure for calculating a weighted average

rating ri by use of the value analysis or cost-benefit analysisintroduced in [27]

ri =n∑

j=1

(wj rij )

/n∑

j=1

wj (1)

where rij denotes the merit of alternative ai according to thecriterion Xj , and wj denotes the importance of criterion Xj

in the evaluation of alternatives. But this procedure is notpossible for the situations where uncertainty exists and theinformation available is incomplete. For example, the terms‘very important’, ‘good’, or ‘not good’ themselves are a fuzzyset. In what follows, the fuzzy ranking problem of a set ofalternatives against a set of criteria is described [17]. Leta set of m alternatives A = {a1, a2, . . . , am} be a fuzzy seton a set of n criteria C = {C1, C2, . . . , Cn} to be evaluated.Suppose that the fuzzy rating rij to certain Xj of alternativeai is characterized by a membership function µRij

(rij ), whererij ∈ R, and a set of weights W = {w1, w2, . . . , wn} arefuzzy linguistic variables characterized by µWj

(wj ), wj ∈ R+.Consider the mapping function gi(zi) : R2n → R defined by

gi(zi) =n∑

j=1

(wj rij )

/n∑

j=1

wj (2)

where zi(w1w2 . . . wn, ri1ri2 . . . rin). We define themembership function µ(zi) by

µZi(zi) = ∧n

j=1 µWj(wj )∧o

k=1,...,n µRik(rik). (3)

Thus, through the mapping gi(zi) : R2n → R, the fuzzy setZi induces a fuzzy rating set Ri with membership function

µRi(ri) = sup

Zig(zi )=ri

µZi(zi), ri ∈ R. (4)

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WebMEMS-MASSGUI

(Netscape or IE)

ProblemSolver

Java

TCP/IP Character Stream

Java

Knowledge Server

Jess/FuzzyJess

User/Designer

Client Applet

MEMS Process and

Material Database

MEMS Process

Selection

Knowledge Base

MEMS Material

SelectionKnowledge Base

Cost Estimation

GUI(Netscape or IE)

ProblemSolver

Java

TCP/IP Character Stream

Java

Knowledge Server

Jess/FuzzyJess

User/Designer

Client Applet

MEMS Process and

Material Database

MEMS Process

Selection

Knowledge Base

MEMS Material

SelectionKnowledge Base

Cost Estimation

Figure 5. Client–knowledge server architecture for MEMS manufacturing advisory service system.

In this case, the final fuzzy rating of design alternative ai canbe characterized by this membership function. But it does notmean the alternative with the maximal µR(ri)is the best one.The following procedures further evaluate the following twofuzzy sets:

(1) a conditional fuzzy set is defined with the membershipfunction;

µI/R(i|ri1 . . . rm)

={

1 if ri > rk, ∀k ∈ (1, 2, . . . , m)

0 otherwise; (5)

(2) a fuzzy set is constructed with membership function

µR(r1, r2, . . . , rm) = ∧oi=1,...,m µRi

(ri). (6)

A combination of these two fuzzy sets induces a fuzzyset I which can determine a best design alternative with thehighest final rating, i.e.

µI (i) = supri1,...,m

µI/R(i|ri1 . . . rm)∧o µR(ri1 . . . rm). (7)

Compared with equation (1), the fuzzy ranking for evaluationand selection is more flexible and presents uncertainty better.Based on this method, the designer can use linguistic rating andweights such as ‘good’, ‘fair’, ‘important’, ‘rather important’,etc, for alternatives evaluation and selection. It therefore looksnatural and attractive in practical use.

5. Web-based MEMS manufacturing advisoryservice system

An integrated expert system could be divided into a two-component architecture with a narrow communication channel[10]. The knowledge–server approach separates the user-interface front end from the problem solver. In this research,a client–knowledge server framework is developed for thedevelopment of a Web-based MEMS manufacturing advisoryservice system (WebMEMS-MASS). Details are discussed inthis section.

5.1. System overview and architecture

WebMEMS-MASS is a Web-based engineering reference toolfor concept level MEMS manufacturing process and materialselection. Based on various input parameters provided bythe remote designer, WebMEMS-MASS determines whichmanufacturing processes are most relevant to the input part.The goal is to provide the designer with knowledge of thefuture production requirements of the part concurrently.The service provides advice that, first, indicates whichmanufacturing process is most suited for the emergingMEMS design and, secondly, how the design could be bestmodified to satisfy the constraints of that particular MEMSprocess. WebMEMS-MASS can also be used as a library ofmanufacturing techniques since it contains detailed websitesfor more than 50 manufacturing processes.

The main components of the proposed client–knowledgeserver architecture for MEMS manufacturing advisory systemare shown in figure 5. The Java-based front-end (left)communicates with the knowledge server (right) through aTCP/IP stream. The knowledge server that is CLIPS-baseduses a Java module for communication with the front-end.Each of these components interact with one another using acommunication protocol (CORBA, Compliant Object RequestBroker) so that it is not required to maintain the elements ona single machine. As a gateway for providing services, thegraphics user interface (GUI) invokes the necessary actionsto provide the requested services. To request a service, thesystem must have an interface pointer to the desired interface.

5.2. System implementation

Initially, a simple demonstrative system was designedas a final-year undergraduate project, implemented usingintelligent website techniques such as form-based commongateway interface (CGI) and Javascript. The formalimplementation of the system uses a Java applet as a front-end to the knowledge server, which provides a Web advisorysystem for process and material selection. The new formalimplementation has several advantages over the originalversion. Two of them are that the system can run on standardhardware and can be available/accessible via the WWW.Moreover, because Java programs are portable, the systemcan run with Web browsers on multiple platforms.

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Figure 6. Startup of WebMEMS-MASS applet.

Figure 6 shows the startup window of the WebMEMS-MASS applet. Java applets allow us to design userinterfaces that are more interactive than CGI-based interfaces.The major purpose is to assist the designer selectingMEMS manufacturing processes and materials or to teachinexperienced designers or students basic skills or knowledgein MEMS manufacturing processes and materials andinteractive rule-based programming, and even allow themto experiment with the knowledge–server approach toimplementing expert systems. When the designer/user haslearned to select manufacturing processes and materials,he/she can continue with the design of a small knowledge basethat performs the task automatically. Thus, the designer/usercan augment the pre-existing system with appropriate databaseand knowledge base that rank and select processes andmaterials. A sample CLIPS rule that is used to select processesis shown as follows:

(defrule Rule X

(goal (type identifyProcess) (value "yes"))

=>

(printout t "Is the process cost high? | explanatory | Answer the question "

"by selecting one of the choices and then clicking on 'proceed'. |yes |no |end")

(assert (attribute (type hasHighcost) (value (readline))))

)

Figure 7 shows fuzzy facts and rules represented inFuzzyJess [25]. To test the selection rules, users can startseveral simulation rules.

5.3. Database and knowledge base for WebMEMS-MASS

WebMEMS-MASS supports many widely used manufacturingprocesses [18, 19, 4], such as plastic injection molding,forging, sand casting, sheet metal forming, extrusion, micromilling, die casting, shell mold casting, investment casting,and electrodischarge machining (EDM). However, it shouldinclude the capabilities of new or less well-known processesat any time if necessary [30]. One of the goals of WebMEMS-MASS is to educate a designer or student about newmanufacturing methods.

The assessment used for the development of WebMEMS-MASS is to create a repository of manufacturing data separatefrom the code for the main program, which requires theremote process capability database containing informationabout the component processes, materials, and vendors.Microsoft Access is selected to develop the relational database.Opening the database on the server brings up a menu of toolswhich include the following components (figure 8) [30, 34]:(1) Vendor Editor, provides the account management forcompanies that have processes; (2) Material Editor, edit

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(deftemplate surfRough0 100 mi

( (very low (0 1) (4 1) (6 0))(low (4 0) (6 1) ( 8 1)(16 0))...(very high (30 1) (>30 1))

high)=>

(printout t "surfRough isacceptable" crlf))

6 8

1

Micro-inches

SurfaceRoughness

4

Very Low Very HighLow Medium High

2816 20 24 30 32

...

...

) )

(defrule surfRough-rule(surfRough not low and not

1

Figure 7. Fuzzy facts and rule in FuzzyJess for selection.

Figure 8. Database administrator tools menu.

Java Application Server

JDBC

Client Applet

Decision Rules

WebPage

AdministrationTools

Complete Access

DB Form

WWW Server

HTML Files

Simple Data

Java Classes

JDBC Bridge

ODBC Source

SQ

L

SQL CallsVendor Applet

JDBC

Capability

WebPage

WebPage

Client Applet

Decision Rules

Process Capability Database

Process Characteristics

AccessDatabase JDBC Java Program User

Java Application Server

JDBC

Client Applet

Decision Rules

WebPage

AdministrationTools

Complete Access

DB Form

WWW Server

HTML Files

Simple Data

Java Classes

JDBC Bridge

ODBC Source

SQ

L

SQL CallsVendor Applet

JDBC

Capability

WebPage

WebPage

Client Applet

Decision Rules

Process Capability Database

Process Characteristics

AccessDatabase JDBC Java Program UserAccessDatabase JDBC Java Program User

Figure 9. Java database system scheme.

the properties of the generic raw materials; (3) FileExporter, generates data files and human readable reports;

(4) Process Editor, specifies the performance of all processes,compatibilities with materials, and locations of on-line

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(a) MEMS process database

(b) Material database

Figure 10. MEMS process and material database Java applet launched.

resources. These tools can add a new process or materialto WebMEMS-MASS databases without any changes to thecompiled code. JDBC (Java Database Connection) is chosenas the method for implementing remote vendors that accessto content in databases [33–37]. A Java database systemwas developed by using a Microsoft Access database to storethe detail information of processes and materials and a Javaprogram to access the database through a JDBC connection.Figure 9 shows a pictorial view of the Java database systemscheme. Two Java applets (figure 10) were developed as wellto access the database from the internet browser. Figure 10(a)also shows a visualization of MEMS material propertiesretrieved from the database.

The knowledge base is actually a rule base to choosemanufacturing processes and materials, which is constructedusing FuzzyJess. Figure 11 shows that the system is loading anexternal knowledge base. The fundamental information thatforms the database of MEMS manufacturing processes andmaterials was obtained from the Internet resources [23, 18,19, 24]. These data and information are used to demonstratethe features of the system and can be replaced by relevantversions when the system is customized for specific use.

5.4. Integration with WebMEMS-Designer system

WebMEMS-MASS is developed mainly for intelligentselection of MEMS manufacturing processes and materials.

Figure 11. Loading of external knowledge base.

It has also been incorporated as a sub-system into a concurrentcollaborative MEMS design system, WebMEMS-Designer[33–37], which is being developed. WebMEMS-Designerhas a unique combination of manufacturing and CAD, whichallows the incorporation of true process data into fabricationsimulation. MEMS Material, a comprehensive materialsdatabase available in [35], provides a vital link between theprocess parameters and the device behavior [18].

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(a) Process Search

(b) Material Search

Figure 12. Process and material search in WebMEMS-MASS.

6. Use of WebMEMS-MASS system

In this section, we present an operation scenario ofWebMEMS-MASS system is presented, and we demonstratehow to use it for manufacturing process and material selection.The comparisons with the current MEMS design systems aregiven.

6.1. Operation scenario

WebMEMS-MASS extends Jess/FuzzyJess, Java ExpertSystem Shell [11, 25] with a GUI, which can be run asa standalone application or as an applet via the WWW.CLIPS or Jess rule bases have been modified slightly soas to work with WebMEMS-MASS. The running sessionsare inter-linked with webpages of tutorials and referencepages explaining the facets, fabrication processes and materialchoices. WebMEMS-MASS performs calculations andreasoning using capability data from a remote Web-baseddatabase that can be maintained and updated via the Internetwith collaborative support. A frame is provided for thefirst-order cost estimation along with examples for selectedprocesses, and the generation of process chains usingsecondary processes to refine certain features on a part. Whilerunning, WebMEMS-MASS generates a dialogue with thedesigner to inquire and acquire about batch size, typical

tolerances, size, overall shape, and cost requirements. Afterentering values for a set of facets, or attributes, for a conceptualpart, the user is given real-time feedback regarding plausiblefabrication methods. Once a process is selected, processchains or cost estimates can be explored [30]. At each stepalong the way, the user is presented with an updated, rankedlist of manufacturing possibilities. A similar method is usedto define the attributes for material selection (yield strength,density, etc), and to generate material rankings. The finalresult is a ranked list of viable combinations, obtained througha process-material pair optimization.

6.2. Support for WebMEMS-MASS

To help users, a step-by-step tutorial provides instructions tousers/learners who are not familiar with manufacturing terms.Samples are offered for an extensive on-line help ‘manual’(see figure 13). Descriptions and sample values are given foreach of the process and material requirements, which allowusers to compare their tolerance values with common products.Each of the processes included in WebMEMS-MASS hasa set of descriptive webpages. The information includesproduction numbers, shape capabilities, design rules, sampleparts, material usage notes, pros/cons, related processes, andlinks to equipment suppliers and fabrication sites. All ofthe documentation is linked through the applet itself. The

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Figure 13. Final results with WebMEMS-MASS for microgripper.

designer may select any processes, materials, or requirements,and click the ‘Get Website Info’ button to call up an informativewebpage. To begin analysis of a design, the user should startthe system. The instruction for the use of the system and foursimple steps are shown in the startup screen in figure 6.

6.3. Illustration example

To illustrate the use of WebMEMS-MASS, a design example ofa prototype microgripper is explored to show the possibilitiesfor making a full production run. The gripper is subject toreal use in the laboratory test for MEMS experiment [31, 37].Thus, it is necessary to use the process search, material search,and results survey mode. The specifications are made for boththe procedures of process search and material search as shownin figures 12(a) and (b), respectively. The process search isfor the lowest possible cost over a long production cycle. Atthe end of the process search, EDM (rank 100%) is aheadof the only other possibility, etch (rank 96%). Similarly, aftermaterial search, the system generates six viable materials, withcarbon steel ranking the highest at 100% and aluminum andalloys ranking at 98%. Furthermore, after process search andmaterial search, the ‘Results Survey’ button would be enabledand clicked to combine the results of both searches to find thebest material/process combination. The two boxes at the top of

figure 13 give a summary of the viable materials and processes.The final box lists all of the feasible combinations, takinginto account a compatibility factor between each process andmaterial. EDM with carbon steel is the best choice, withetched stainless steel second.

6.4. Results and discussion

This work differs from existing commercial MEMS designsystems such as COVENTORWARETM (MEMCADTM),IntelliSuiteTM and MEMSCAPTM, which support integrateddesign and process modeling. WebMEMS-MASS isknowledge-based, and embodies an effective and efficientmethod and mechanism to select MEMS manufacturingprocesses and materials and their combinations. The systemcan provide an advisory service and explain the results andwhat-ifs. Specifically, it is able to provide a common languageat the concept level, allowing a designer to describe a part sothat an expert advisory/consultant system can decide whichmanufacturing processes/materials can produce the desiredpart, in the desired time, with the desired quality. This meansthat WebMEMS-MASS is designed as a tool for finding agood fabrication method for a part while still at the conceptuallevel of design, and making a diverse catalog of processingcapabilities available to designers/users so that they can

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experiment with different fabrication technologies. Newprocesses that have not yet achieved widespread understandingin the engineering community can also make their debutthrough such service system.

The widespread use of WebMEMS-MASS is likely tolead many material suppliers to put database searches on-line,allowing users to filter inventories based on user-enteredmaterial property ranges. Also, WebMEMS-MASS allowsdevelopers to provide intelligent knowledge services and anopen environment to support and coordinate highly distributedand decentralized collaborative MEMS design and modelingactivities for designers/users. The Web-based interface letsdesigners/users assemble process sequences and submit themfor review by MEMS engineers and fabrication sites ifnecessary. Thus, WebMEMS-MASS provides remote usersadvice that: (1) indicates which manufacturing process isthe most suited to the emerging design; (2) shows how thedesign could best be modified to satisfy the constraints of thatparticular process.

7. Summary and future work

In this paper we have presented our preliminary work on thedevelopment of the knowledge intensive method and systemfor MEMS manufacturing process and material selection,which bring together engineering reference material and aninformative education or learning tool over the Internet andWWW. The reference materials include basic processdescriptions, special abilities, some simple design rules, andlinks to fabrication sites. The Java-based WebMEMS-MASScan provide a knowledge intensive intelligent dynamicenvironment for educating designers/students about thetrade-offs available in different manufacturing processes.The applet of WebMEMS-MASS is potentially available toanyone, at any time and anywhere with a Java compatiblebrowser so as to work in most computing environments. Thesystem can be used for simple single parameter searches toselect a process, and also process and material combinedsearches with secondary processes mapped to high-tolerancefeatures. The rankings change in real time with user inputbut without the need to query a database for results [30].The underlying WebMEMS-MASS databases and knowledgebases are extensible through administrator tools or via the Webwhich gives commercial manufacturing facilities the ability toupdate their own processes and rules. The designer/studentcan also submit on-line external manufacturing process andmaterial selection knowledge bases for some specific newprocesses. A large amount of the future work will be dedicatedto the enhancement of process and material databases andknowledge base and the further development of the system.

Acknowledgments

The authors would like to express their gratitude to anonymousreviewers of this paper for their insightful comments andsuggestions that have helped to improve the paper.

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