Browsing by Author "Lin, C. -M."

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Influence of annealing temperature on the structural, optical and mechanical properties of ALD-derived ZnO thin films
(2011-06-15)
Yen, C. -Y.
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Jian, S. -R.
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Chen, G. -J.
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Lin, C. -M.
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Lee, H. -Y.
ZnO thin films grown on Si(1 1 1) substrates by using atomic layer deposition (ALD) were annealed at the temperatures ranging from 300 to 500 degrees C. The X-ray diffraction (XRD) results show that the annealed ZnO thin films are highly (0 0 2)-oriented, indicating a well ordered microstructure. The film surface examined by the atomic force microscopy (AFM), however, indicated that the roughness increases with increasing annealing temperature. The photoluminescence (PL) spectrum showed that the intensity of UV emission was strongest for films annealed at 500 degrees C. The mechanical properties of the resultant ZnO thin films investigated by nanoindentation reveal that the hardness decreases from 9.2 GPa to 7.2 GPa for films annealed at 300 degrees C and 500 degrees C, respectively. On the other hand, the Young's modulus for the former is 168.6 GPa as compared to a value of 139.5 GPa for the latter. Moreover, the relationship between the hardness and film grain size appear to follow closely with the Hall-Petch equation. (C) 2011 Elsevier B. V. All rights reserved.
Supervisory intelligent control system design for forward DC-DC converters
(2006-09-01)
Hsu, C. -F.
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Lin, C. -M.
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Cheng, K. -H.
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電控工程研究所
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Institute of Electrical and Control Engineering
A supervisory intelligent control system is developed. The supervisory intelligent control system is comprised of a neural controller and a supervisory controller. The neural controller is investigated to mimic an ideal controller and the supervisory controller is designed to compensate for the approximation error between the neural controller and the ideal controller. In the proposed control scheme, an online parameter training methodology is developed based on the gradient descent method and the Lyapunov stability theorem, so that the control system can guarantee system stability. Finally, to investigate the effectiveness of the proposed control scheme, it is applied to control a forward DC-DC converter. A comparison between a PI controller, a fuzzy controller, a fuzzy neural network controller and the supervisory intelligent controller is made. Experimental results show that the proposed control system can achieve favourable regulation performances even for different input voltages and under load resistance variations.