Browsing by Author "Chang, Wen-Jung"

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Intramolecular Locked Dithioalkylbithiophene-Based Semiconductors for High-Performance Organic Field-Effect Transistors
(2017-09-20)
Vegiraju, Sureshraju
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Chang, Bo-Chin
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Priyanka, Pragya
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Huang, Deng-Yi
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Wu, Kuan-Yi
New 3,3-dithioalkyl-2,2-bithiophene (SBT)-based small molecular and polymeric semiconductors are synthesized by end-capping or copolymerization with dithienothiophen-2-yl units. Single-crystal, molecular orbital computations, and optical/electrochemical data indicate that the SBT core is completely planar, likely via S(alkyl)S(thiophene) intramolecular locks. Therefore, compared to semiconductors based on the conventional 3,3-dialkyl-2,2-bithiophene, the resulting SBT systems are planar (torsional angle <1 degrees) and highly -conjugated. Charge transport is investigated for solution-sheared films in field-effect transistors demonstrating that SBT can enable good semiconducting materials with hole mobilities ranging from approximate to 0.03 to 1.7 cm(2) V-1 s(-1). Transport difference within this family is rationalized by film morphology, as accessed by grazing incidence X-ray diffraction experiments.
Product-mix decision in a mixed-yield wafer fabrication scenario
(2006-07-01)
Wu, Muh-Cherng
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Chang, Wen-Jung
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Chiou, Chie-Wun
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工業工程與管理學系
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Department of Industrial Engineering and Management
The product mix decision problem for semiconductor manufacturing has been extensively studied in literature. However, most of them are based on a high-yield scenario. Yet, in a low-yield manufacturing environment, some research claims that scrap low-yield lots in an early stage may produce more profit. Considering the early scrapping characteristics, this paper aims to solve the product mix decision problem for a mixed-yield scenario, which involves the simultaneous production of high-yield and low-yield products. A nonlinear mathematical program is developed to model the decision problem. Two methods for solving the nonlinear program are proposed. Method 1 converts the nonlinear program into a linear program by setting some variables as parameters. The method provides an optimal solution by exhaustively searching these parameterized variables and solving the LP models iteratively. Method 2 aims to reduce the computation complexity while providing a near optimal solution. Experiment results show that method 2 is better than method 1, when aggregately considering solution quality and computation efforts.