• JSTS:Journal of Semiconductor Technology and Science
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• v.3 no.2
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• pp.83-88
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• 2003

The reset operation of a CCD image sensor was improved using charge trapping of a MOS structure to realize a loe voltage driving. A DC bias generating circuit was added to the reset structure which sets reference voltage and holds the signal charge to be detected. The generated DC bias is added to the reset pulse to give an optimized voltage margin to the reset operation, and is controlled by adjustment of the threshold voltage of a MOS transistor in the circuit. By the pulse-type stress voltage applied to the gate, the electrons and holes were injected to the gate dielectrics, and the threshold voltage could be adjusted ranging from 0.2V to 5.5V, which is suitable for controlling the incomplete reset operation due to the process variation. The charges trapped in the silicon nitride lead to the positive and negative shift of the threshold voltage, and this phenomenon is explained by Poole-Frenkel conduction and Fowler-Nordheim conduction. A CCD image sensor with $492(H){\;}{\times}{\;}510(V)$ pixels adopting this structure showed complete reset operation with the driving voltage of 3.0V. The resolution chart taken with the image sensor shows no image flow to the illumination of 30 lux, even in the driving voltage of 3.0V.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.657-676
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• 2011

Properties of statistical process control procedures may not be derived analytically in many cases; however, the application of a Markov chain can solve such problems. This article shows how to derive the properties of the process control procedures using the generated Markov chains when the control statistic satisfies the Markov property. Markov chain approaches that appear in the literature (such as the statistical design and economic design of the control chart as well as the variable sampling rate design) are reviewed along with the introduction of research results for application to a new control procedure and reset chart. The joint application of a Markov chain approach and analytical solutions (when available) can guarantee the correct derivation of the properties. A Markov chain approach is recommended over simulation studies due to its precise derivation of properties and short calculation times.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.537-556
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• 2021

Properties of statistical process control procedures may not be derived analytically in many cases; however, the application of a Markov chain can solve such problems. This article shows how to derive the properties of the process control procedures using the generated Markov chains when the control statistic satisfies the Markov property. Markov chain approaches that appear in the literature (such as the statistical design and economic design of the control chart as well as the variable sampling rate design) are reviewed along with the introduction of research results for application to a new control procedure and reset chart. The joint application of a Markov chain approach and analytical solutions (when available) can guarantee the correct derivation of the properties. A Markov chain approach is recommended over simulation studies due to its precise derivation of properties and short calculation times.