Editorial e-Manufacturing in the Semiconductor Industry

Zhang, Mike Tao; Fowler, John W.; Chen, Thomas W. Y.; Shanthikumar, J. George; Chien, Chen-Fu

doi:10.1109/tase.2007.907381

Cited by 7 publications

(4 citation statements)

References 1 publication

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“…E-manufacturing integrates hardware controls, information systems, and advanced decision technologies such as industrial engineering, operations research, artificial intelligence, and queuing theory [5]. E-manufacturing optimises the use of production assets based on information exchange from shop-floor operations across the enterprise to the whole supply chain [6].…”

Section: E-manufacturingmentioning

confidence: 99%

Towards the Implementation of E-Manufacturing: Design of an Automatic Tea Drying Control System

Mabvuu¹,

Nyanga

et al. 2014

SAJIE

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Many of the production costs for producing tea are attributable to the process of drying the tea. E-manufacturing can assist companies to reduce these production costs by making crucial information available to decision-makers so that they can make informed decisions. This paper presents an application of e-manufacturing to the design of an automatic tea drying control system. This control system will ensure that the multiple drying parameters such as temperature, dryer-exit tea moisture content, and fuel consumption are maintained at optimal states during the course of the drying of tea. The additional aim of this system is to balance the cost of production and the quality of the final product. Using the Guggenheim-Anderson-De Boer (GAB) model, the optimum drying temperature was found to be 100-110°C, while maintaining a dryer-exit tea moisture content of 3 to 3.12 per cent, at a drying rate of 3 per cent per minute. A Barix control application to control the system's activities, using the web user interface (WUI), was also developed. OPSOMMING'n Groot gedeelte van die koste in die produksie van tee is as gevolg van die teedroogproses. E-vervaardiging kan die produksiekoste verminder deur kritiese inligting vir die besluitnemers beskikbaar te stel. Hierdie studie beskryf 'n toepassing van e-vervaardiging op die ontwerp van 'n outomatiese tee-droog beheerstelsel. Die beheerstelsel verseker dat al die droogparameters, soos temperatuur, voggehalte en brandstofverbruik, by optimum toestande beheer word deur die loop van die droogproses. Verder word daar deur die beheerstelsel 'n balans tussen die produksiekoste en die gehalte van die finale produk gehandhaaf. Deur van die Guggenheim-Anderson-De Boer (GAB) model gebruik te maak, is die optimale droogtemperatuur gevind om tussen 100 tot 110°C te lê, terwyl die droëruitlaatvoggehalte tussen 3 en 3.12% met 'n droogtempo van 3% per minuut te lê. 'n Barix beheer program is ontwikkel om die beheerstelsel se werksaamhede te monitor.2 The paper was written in support of L. Nyanga's PhD research on the development of an emanufacturing model for the South African industry.

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Section: E-manufacturingmentioning

confidence: 99%

Towards the Implementation of E-Manufacturing: Design of an Automatic Tea Drying Control System

Mabvuu¹,

Nyanga

et al. 2014

SAJIE

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“…Lastly, each chip goes through final product testing and prepared for sale. Wafer fabrication is characterized with complex production processes, advanced equipment, and volatile demand [16]. Wafer fabrication facility (fab) requires huge capital investment.…”

Section: Wafer Fabrication Yieldmentioning

confidence: 99%

“…9 illustrates the compound yield learning curve per (16) with N = 50, c i = 1 (A i = A i+1 , ∀i = 1, 2, ..., N − 1). Its significance is in exhibiting a sigmoid curve composed of the product of individual steps learning curves.…”

Section: B Compound Yield Learning Curvementioning

confidence: 99%

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Yield Learning Curve Models in Semiconductor Manufacturing

Tirkel

2013

IEEE Trans. Semicond. Manufact.

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Semiconductor manufacturing is a constantly growing business characterized with complex production processes, advanced equipment, and volatile demand. In wafer fabrication where die are produced, good and defected die are combined on the same wafer. The proportion between good and total die, noted (die) yield, is a key performance measure in operations and has a dominant effect on manufacturing economics. Integrated circuits life cycle commonly starts with low yields that should be significantly increased and then maintained at a high level, to maximize profit. Traditional manufacturing learning curves are in the form of a power function, and exhibit decreasing cost as a function of cumulative output. This study suggests generalized multi-factor learning curves composed of power and exponential functions of cumulative output, elapsed time, and production rate. It presents a unique approach to develop compound yield learning model as a product of individual steps yield learning curves, rather than displaying total yield in former models. The proposed single step yield learning curve applies more suitable exponential function, and the compound yield product-form model illustrates sigmoid shape curve which better coincides with practice. These models provide yield forecasting tools for improving short term operations planning and supply chain efficiency, and for setting strategic directions for wafer fabrication economics.

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Mechatronic Futures

Russell¹,

Bradley²

2010

Mechatronics in Action

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Editorial e-Manufacturing in the Semiconductor Industry

Cited by 7 publications

References 1 publication

Towards the Implementation of E-Manufacturing: Design of an Automatic Tea Drying Control System

Towards the Implementation of E-Manufacturing: Design of an Automatic Tea Drying Control System

Yield Learning Curve Models in Semiconductor Manufacturing

Mechatronic Futures

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