920-924In this study, response surface methodology (RSM) andartificial neural network (ANN) were applied to predict material removal rate in chemical etching process of alumino-silicate glass (SiO2 57/Al2O3 36/CaO/MgO/BaO). 2k Factorial design was performed to evaluate linearity condition among process parameters. Analysis of variance (ANOVA) was performed and quadratic model was found most significant for data values of process parameters. New models were able to predict etching rate of alumino-silicate glass, with a great confidence. Input parameters analyzed were temperature, etching period and type of setup with and without condensation

Abou-El-Hossein, K.

Chua, Han

Ting, Huey Tze

English

espace@Curtin

920 J SCI IND RES   VOL 68   NOVEMBER 2009*Author for correspondenceE-mail: ting.huey.tze@stud.curtin.edu.myPrediction of etching rate of alumino-silicate glass by RSM and ANNH T Ting1*, K A Abou-El-Hossein2 and H B Chua11Mechanical Engineering Deparment, Curtin University of Technology (Sarawak Campus), 68006 Miri, Sarawak2Department of Mechatronics, Nelson Mandela Metropolitan University, Port Elizabeth 6031, South AfricaReceived 25 March 2009; revised 24 July 2009; accepted 27 July 2009In this study, response surface methodology (RSM) and artificial neural network (ANN) were applied to predict materialremoval rate in chemical etching process of alumino-silicate glass (SiO2 57/Al2O3 36/CaO/MgO/BaO). 2k Factorial design wasperformed to evaluate linearity condition among process parameters. Analysis of variance (ANOVA) was performed and qua-dratic model was found most significant for data values of process parameters. New models were able to predict etching rate ofalumino-silicate glass, with a great confidence. Input parameters analyzed were temperature, etching period and type of setupwith and without condensation.Keywords: Alumino-silicate glass, ANN, Etching rate, RSMIntroductionAlumino-silicate glass (ASG) is frequently used insemiconductor and optical industry and to controlrelease of volatile chemicals. Due to its insensitivity tothermal effect, ASG is used as doped fiber in all-opticaldevices1. It is also widely used in micro-electromechanical system (MEMS) due to its good durability,smoothness, high electrical property, and high opticaltransparency2,3. Saito et al4 claimed that etching rate ofASG increased with increasing leaching reaction ofalumina. Saito et al5 also investigated effects of glasscomposition and etching conditions on etching rate withpH, concentration and temperature of solution.Bezerra et al6 summarized that application ofDesign of Experiment (DOE) in optimization was largelydiffused and consolidated principally because of itsadvantages over experimentation approach that is basedon classical one-variable-a-time optimization. Hunget al7 applied response surface methodology (RSM) toinvestigate effect of various controlled factors onperformance of silicon trench etches on Cl2/HBr/O2based chemistry. Dabnun et al8 applied RSM inpredicting surface roughness of machinable glassceramic. DOE was also applied in industry formodeling and optimization of chemical etching processin niobium cavities9.Artificial neural network (ANN) models hadconsistent results compared to RSM10,11. Desai et al12concluded that ANN perform at better accuracy andgeneration capability as compared to RSM. Fredj et al13also found that ANN had consistently performed betterthan RSM. Ko et al14 evaluated growth rate of ZnO thinfilm fabricated by D-optimal and ANN. Kim et al15 usedANN to model characteristics of oxide film etched inCHF3/CF4 gas chemistry and examined etch mechanismsas a function of process factors. Shie & Yang16 focusedon optimizing photoresist coating process using ANN withonly nine experimental runs, in order to identify processparameters in wafer fabrication. However, there is lackof studies on chemical etching of of ASG using RSM andANN model.This study presents predictive model of ASGetching rate with Central Composite Design (CCD) atdifferent levels and ranges of variables with right designof experiment and set up types using RSM, ANN andANOVA.ExperimentalAmong chemical resistant characteristics, ASG isfair to concentrated acid and good to dilute acid andalkalis. Physico-thermal properties of ASG are as follows:apparent porosity, 45%; density, 2.65 g/cm; refractiveindex, 1.54; coefficient of thermal expansion, 5; uppercontinuous use temperature, 600°C. Etching experimentswere carried out on ASG to observe etching rate underJournal of Scientific & Industrial ResearchVol. 68, November  2009, pp. 920-924TING et al: PREDICTION OF ETCHING RATE OF ALUMINO-SILICATE GLASS BY RSM AND ANN         921condensation, without condensation, two levels ofetching temperature and etching period. Middle pointswere added in these experiments to predict curvaturesurface using quadratic model and estimation of error inexperiment data. Samples were etched in 12 M HCl,then cleaned up and rinsed with continuous distilledwater. After 1 h, samples were baked at 110°C for about1 h before recording data. Etching rate was calculatedby dividing reduced weight (g) by etching period (min).Obtained data was mapped using 2k Factorial design,RSM and ANN.Design of ExperimentThis experiment design is divided into 2k Factorialdesign for first-order model and RSM for second-ordermodel. After determination of first stage data, ANOVAis performed. At this stage, checking at curvature termis extremely important. A significant curvature termindicate quadratic or higher-level process model for nextCCD. Otherwise, first stage data obtained by 2 kFactorial Design have to be analyzed in case of insignificant curvature term indicating linear process model.In terms of time and cost saving, 2k Factorial design is amust procedure and it also helps to provide right rangesof process parameters subjected to design ofexperimentation. Total numbers of runs are 27 and allrunning sequences have been randomized and generatedfollowing CCD procedure. With three inputs, DE 7generated 13 numbers of test combinations to study wholeprocess. Design Expert 7.0 (DE 7) was used to performtask and calculations.Results and DiscussionFirst-order Model (2k Factorial Design)ANOVA generated for 2k Factorial Design(Table 1) shows that curvature is a significant term inetching process of ASG. This implies that a quadraticmodel should be considered to model this process. Cur-vature value (0.0002) is less than 0.05, indicating that only0.02% of process is fitted to linear process and there isTable 1  Analysis of variance (ANOVA) for first-order modelSource Sum of df Mean F value p-valuesquares square Prob  FModel 0.0600 7 0.013 36.53 0.0001 SignificantA-condensation 0.0028 1 0.0028 8.66 0.0257B-Temperature 0.0052 1 0.0051 15.60 0.0075C-Period 0.0074 1 0.0074 226.73  0.0001AB 0.0017 1 0.0027 8.31 0.0276AC 0.0013 1 0.0027 4.05 0.0608BC 0.0014 1 0.0013 4.23 0.0855ABC 0.0032 1 0.0014 6.75 0.0205Curvature 0.0300 2 0.0150 46.66 0.0002 SignificantPure error 0.0016 6 0.0003Cor total 0.1200 15Table 2  Model sum of squaresSource Sum of df Mean F value p-valuesquares square Prob  F2FI vs Linear 0.00660 3 0.00216 1.01 0.4078Quadratic vs 2FI 0.02100 2 0.01000 8.36 0.0027 SuggestedCubic vs Quadratic 0.01300 5 0.00252 3.31 0.0376 AliasedTable 3  Model summary statisticsSource Std. R-Squared Adjusted Predicted Pressdeviation R-squared R-squaredLinear 0.047 0.6413 0.5646 0.4433 0.0772FI 0.047 0.6886 0.5652 0.1880 0.110Quadratic 0.035 0.8386 0.7666 0.4566 0.075 Suggested922 J SCI IND RES   VOL 68   NOVEMBER 200966.668% confidence in possible fitting of this processinto higher curvature form (quadratic, cubic or higherlevel). Therefore, RSM is needed to apply in order todetermine quadratic model for the process.Second-order Model (using RSM)In order to obtain second order predictive model,RSM is employed using CCD, which is a design typeunder RSM. This is chosen due to its flexibility. In CCD,process variables’ peaks occur simultaneously. ANOVAis essential to decide if process fits to selected model ornot. Tables 2-4 show model sum of squares, model sum-mary statistics and ANOVA, respectively.At level of 95% of confidence, process was checkedfor its adequacy and quadratic model is selected insteadof linear or cubic model. It was found (Table 4) that thisquadratic model is fitted into the process. Dominantfactors have been determined (Table 4) by evaluatingtheir p-values. Once process model has been confirmed,next stage will be monitoring changes of each dominantfactor by examining generated graphs. P-value showssignificance of corresponding factors. Smaller themagnitude of p-value, more significant the model is. Inthis process, it is clearly shown that temperature is mostdominant factor followed by etching period and then, setup condition. Predictive models are given as follows:Etching rate with condensation (g/min) =0.325–0.0077(t)–0.015(T)+0.00003(t)(T)+ 0.0017(T2) …(1)Etching rate without condensation (g/min)=0.363–0.0078(t) –0.016(T)+0.00003(t)(T)+0.0017(T2) …(2)where, t is etching  period (min) and T is etchingtemperature (°C).Least dominant factor found to have only 28.3%influence on the process, whose p-value is larger thanTable 4 ANOVA for ASG etching in12 M HClSource Sum of df Mean     F p-valuesquares squares value Prob  FBlock 0.00067 1 0.00067Model 0.12000 8 0.01500 11.66  0.0001 SignificantA-Etching period 0.00176 1 0.00176 1.44 0.2466B-Etching temperature 0.04300 1 0.04300 34.35  0.0001C-Set up (with and    without condensation) 0.00017 1 0.00017 0.14 0.7170AB 0.00345 1 0.00345 2.76 0.1137Residual 0.022 18 0.00127Lack of fit 0.013 7 0.00185 2.14 0.1244 InsignificantPure error 0.006 11 0.00086Cor total 0.14 27Fig. 1 — Etching rate vs temperature with different set up Fig. 2 — Etching period vs etching rate at 45oCEtch rate, g/minTemperature, oCA: periodEtch rateTING et al: PREDICTION OF ETCHING RATE OF ALUMINO-SILICATE GLASS BY RSM AND ANN         9230.05 (Table 4). Thus, setup condition is an insignificantfactor and decision shall be made in order to reducetesting number in further study. Higher etching rate isobserved to take place at setup without refluxingcondition (Fig. 1). In setup with condensation, itsconcentration remained same during the process.Vaporized H2O is being condensed and return to solutionduring experiment. While, this phenomenon does nothappen at setup without condensation. Especially attemperature near to solution boiling point, water content in solution tended to evaporate into the air. Thisphenomenon is reducing H2O in HCl and increasingconcentration too. Higher etching rate was observed atsetup without condensation, also in agreement with re-ported studies5,17. Etching rate of ASG increases withetching period (Fig. 2). Yet, this is a quadratic reaction.Once it meets peak point, it will stop or go backward dueto chemical reagent, also in agreement with reportedstudies18. Etching rate increases with increasing etchingtemperature (Fig. 3). Highest etching rate happens atright of the graph, which is not a peak value. Hence,further study need to be conducted in order to obtain itsyield point. At higher temperatures, activation energy ofsolution content is higher and this will allow chloric ion toreact with ceramic surface. Contour plot (Fig. 4) showsrelationship between temperature, period and etching rate.Highest etching rate occurs at right corner of contour athighest temperature and period. Unfortunately, contourhas not reached its peak. Thus, further study need to beconducted in order to obtain yield point for eachinfluencing factors.A good agreement with experimental result isobserved for ANN prediction (Fig. 5). Quadratic modelof RSM was able to predict etching rate. It was foundthat these techniques are showing great promising resultwith only 1.12% (CCD) and 1.05% (ANN) error. ANNhad been justified as best tool to be used to predict thisprocess.Fig. 3 — Etching temperature vs etching rateEtch rateTemperatureEtch rateFig. 4 — Contour plot of ASG etching rate vs temperature and period924 J SCI IND RES   VOL 68   NOVEMBER 2009ConclusionsChemical etching process of ASG was investigatedin this work using RSM and ANN. Model produced byRSM was adequately verified at 95% of confidenceinterval. ANOVA showed that etching process of ASGwas quadratic with etching temperature and etchingperiod having highest dominance as input factors.Generated values by RSM model showed a goodagreement with those received experimentally and byANN. However, ANN showed more precise predictionsas compared to RSM model. Non-condensation setupgave best etching rate at 60oC and 180 min. Yet, changesof solution content should be taken into consideration.AcknowledgementAuthors thank Ministry of Science, Technology andInnovation (Malaysia) for financial support under eScienceProgramme.References1 Kim Y H, Kim N S, Chung Y, Paek U –C & Han W –T, All-optical switching application based on optical nonlinearity ofYb3+ doped ASG fiber with a long-period fiber gratings pair,Optics Express, 12 (2004) 651-656.2 Lee J -H, Seo Y, Lim T -S, Bishop P L & Papautsky I, MEMSneedle-type sensor array for in situ measurements of dissolvedoxygen and redox potential, Environ Sci Technol, 41 (2007) 7857-7863.3 Akashi T & Yoshimura Y, Deep reactive ion etching ofboronsilicate glass using an anodically bonded silicon wafer as anetching mask, J Micromech Microeng, 16 (2006) 1051-1056.4 Saito Y, Okamoto S, Inomata H & Kurachi J, Mechanism of theetching rate change of aluminosilicate glass in HF acid with mi-cro-indentation, Appl Surface Sci, 255 (2008) 2260-2264.5 Saito Y, Okamoto S, Inomata H, Kurachi J, Hidaka T & Kasai H,Mircro-fabrication techniques applied to aluminosilicate glasssurfaces: micro-indentation and wet etching process, Thin SolidFilms, 517 (2009) 2900-2904.6 Bezerra M A, Santelli R E, Oliveira E P, Villar L S & Escaleira LA, Response surface methodology (RSM) as a tool for optimiza-tion in analytical chemistry, Talanta, 76 (2008) 665-677.7 Hung C C, Lin H C & Shih H C, Response surface methodologyapplied to silicon trench etching in Cl2/HBr/O2 using transformercouple plasma technique, Solid-State Electron, 46 (2002) 761-765.8 Dabnun M A, Hashim M S J & El-Baradie M A, Surface rough-ness prediction model by design of experiments for turning ma-chinable glass-ceramic (Macor), J Mater Process Technol, 164-165 (2005) 1286-1263.9 Subramaniam S, Xue Q, Trabia M, Chen Y & Schill Jr R, Mod-elling and optimization of the chemical etching process in nio-bium cavities, in Proc Int Congress on Advanced Nuclear PowerPlants (Hollywood) 2002, 1-6.10 Kadirgama K & Abou-El-Hossein K A, Prediction of torque inmilling by response surface methodology and neural network, IntJ Modelling Simulation, 28 (2008) 1-7.11 Koksal M A & Ugursal V I, Comparison of neural network,conditional demand analysis, and engineering approaches for mod-elling end-use energy consumption in the residential sector, ApplEner, 85 (2008) 271-266.12 Desai K M, Survase S A, Saudagar P S, Lele S S & Singhal R S,Comparison of artificial neural network (ANN) and responsesurface methodology (RSM) in fermentation media optimiza-tion: Case study of fermentation production of scleroglucan,Biochem Engg J, 41 (2008) 266-273.13 Fredj N B, Amamou R & Rezgui M A, Surface roughness predic-tion based upon experimental design and neural network models,in Proc of 25th IASTED Int Multi-Conf: Artificial Intelligence andApplication (Austria) 2002, 96-101.14 Ko Y -D, Moon P, Kim C E, Ham M -H, Myoung J –M & YunI, Modeling and optimization of the growth rate for ZnO thinfilms using neural networks and genetic algorithms, Expert SystApplic, 36 (2006) 4061-4066.15 Kim B, Kwon K -H, Kwon S -K, Park J -M, Yoo S W, Park K -S, You I –K & Kim B -W, Modeling etching rate and uniformityof oxide via etching in a CHF3/CF4 plasma using neural network,Thin Solid Films, 426 (2003) 8-15.16 Shie J –R & Yang Y -K, Optimization of a photoresist coatingprocess for photolithography in wafer manufacture via a radialbasis neural network: a case study, Microelectron Engg, 85 (2008)1664-1670.17 Eki M & Takayuki S, Micromachining of fine ceramics by pho-tolithography, Sensors Actuators A, 75 (1999) 278-288.18 Argiolas N, Bazzan M, Bemardi A, Cattaruzza E, Mazzoldi P,Schiavuta P, Sada C & Hangen U, A systematic study of the00.050.10.150.20.250.31 4 7 10 13 16 19 22 25 28ExperimentalANNRunDesired output and actual outputEtching rate, g/minFig. 5 — ANN prediction result        chemical etching process on periodically poled lithium niobatestructure, Mater Sci Engg B, 118 (2005) 150-154.

Prediction of Etching Rate of Alumino-Silicate Glass by RSM and ANN

Ting, H T

Abou-El-Hossein, K A

Chua, H B

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Prediction of etching rate of alumino-silicate glass by RSM and ANN

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Prediction of Etching Rate of Alumino-Silicate Glass by RSM and ANN

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