South African Journal of Industrial Engineering

Wafer yield is a key indicator to pursuing excellence in semiconductor manufacturing. With the increased wafer size, the enhanced complexity and precision of wafer fabrication is possible. Using monitoring to improve the process by predicting the yield has become an important quality issue. Most research uses the number of wafer defects, the area of the wafer, and fixed statistical distribution to predict the yield. Such methods fail to establish a high yield model due to the random and system-wide distribution of wafer defects. This study proposes the Mahalanobis-Taguchi system (MTS) to determine the key variables from the wafer acceptance test (WAT), and establish a classification model of yield grade. The general regression neural network (GRNN) was used to build a predicted model of the wafer probe yield from selected common variables. A real case from a Taiwan manufacturer of dynamic random-access memory (DRAM) is used as an example. It can get the 82 key and significant sequence variables of the WAT, with classification precision of over 90% and the R² of the GRNN prediction model at 0.73. Through demonstration, the result can effectively increase the yield and reduce the quality cost in DRAM manufacturing.

Flinteropbrengs is ʼn sleutel indikator vir halfgeleiervervaardiging. Die toename in flintergrootte maak dit moontlik om die kompleksiteit en presisie van flintervervaardiging te verbeter. Wanneer monitering gebruik word om die opbrengsskatting te verbeter word dit ʼn gehalte kwessie. Die meeste navorsing gebruik die aantal defekte, die flinteroppervlakte, en vasgestelde statistiese verdelings om die opbrengs te skat. Sulke metodes faal egter om ʼn hoë-opbrengs model te bewerkstellig as gevolg van die lukrake en stelselwye verspreiding van defekte. Hierdie studie gebruik die Mahalanobis-Taguchi stelsel om die sleutel veranderlikes van die flinter aanvaardingstoets te bepaal en skep dan ʼn klassifikasie model van die opbrengsgehalte. ʼn Veralgemeende regressie neurale netwerk is gebruik om ʼn skatting van die opbrengs te maak van gekose gemeenskaplike veranderlikes. ʼn Gevallestudie van ʼn Taiwannese vervaardiger van dinamies ewetoeganklike geheue word as voorbeeld gebruik. Die benadering kan twee-en-tagtig van die sleutel en noemenswaardige veranderlikes van die aanvaringstoets identifiseer met ʼn klassifikasiepresisie van meer as negentig persent. Die R²-koëffisiënt van die neurale netwerk is 0.73. Die resultaat kan die opbrengs verhoog en die gehaltekoste van dinamies ewetoeganklike geheue vervaardiging verminder.

Lean manufacturing is becoming the frontrunner in solving manufacturing-related problems. To sustain the lean journey, assessing lean readiness is the appropriate way to implement it in any industry. In this research, lean readiness is evaluated in a case study industry using a framework model. It consists of two dimensions: the critical success factors, and the barriers. Using force field analysis, the conflicting nature of these dimensions was portrayed. With the help of fuzzy logic, a hierarchical fuzzy system was developed to evaluate lean readiness. The result of this method opens up a new direction for managers to identify lean readiness.

Lenige vervaardiging is die voorloper om vervaardiging verwante uitdagings aan te spreek. Dit is egter belangrik vir volhoubaarheid om die gereedheid om van lenige vervaardiging gebruik te maak, te bepaal. Lenige gereedheid word deur middel van ʼn gevallestudie beoordeel met die gebruik van ʼn raamwerk model. Dit bestaan uit twee dimensies, naamlik die kritiese suksesfaktore en die hindernisse. Die teenstrydige aard van hierdie twee faktore is uitgebeeld deur kragveldanalise. Met die hulp van die wasigheidsleer is ʼn wasige hiërargiese stelsel ontwikkel om lenige gereedheid te evalueer. Die resultaat van hierdie metode skep ʼn nuwe metode vir bestuurders om lenige gereedheid te identifiseer.

Costing is important for manufacturing industries. Large methods of costing have evolved over time. Recently, the time-driven activity-based costing (TDABC) system has gained importance and application. This article describes the procedure that allows companies to implement TDABC using the Maynard operation sequence technique (MOST) for improving productivity and profitability. Two parameters are required for TDABC: (1) the unit cost of supplying capacity, and (2) the time required to perform a transaction or an activity. MOST is employed to estimate the time required for each activity. Based on this, time equations are formulated and the practical capacity of activities is determined. The procedure is explained with the help of a case study from a manufacturing industry. The results of the case study are discussed from the perspective of the overall company and also at the product level. This approach provides the capacity analysis and the cost analysis together with its hierarchical decomposition. This paper also discusses the different information obtained from TDABC, and its usefulness for managers and decision-makers.

Kosteberaming is belangrik vir die vervaardigingindustrie. Verskeie kosteberamingmetodes is met die verloop van tyd ontwikkel. Tydgedrewe aktiwiteitsgebaseerde kosteberaming (TDABC) het onlangs aan die lig gekom en word in hierdie artikel bespreek, spesifiek met die klem op die prosedure om TDABC te implementeer deur middel van die Maynard operasie sekwensiële tegniek (MOST) om sodoende produktiwiteit en winsgewendheid te verbeter. Twee parameters word vir TDABC benodig, naamlik (1) die eenheidskoste van voorsieningskapasiteit en (2) die tyd benodig om ʼn transaksie of aktiwiteit te verrig. MOST word ingespan om die tyd vir elke aktiwiteit te skat. Op grond hiervan word tydvergelykings opgestel en die praktiese kapasiteit van aktiwiteite bepaal. Die prosedure word toegelig met die hulp van ʼn gevallestudie vanuit die vervaardigingindustrie. Die gevallestudie resultate is bespreek vanuit die algehele maatskappy se perspektief asook op produkvlak. Hierdie benadering verskaf die kapasiteit en die koste analise saam met die hiërargiese samestelling. Die artikel bespreek ook die informasie wat vanuit die TDABC verkry word en die nut daarvan vir bestuurders en besluitnemers.