Browsing by Author "Chang, Chun-Lung"

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A Metal-Insulator-Semiconductor Solar Cell With High Open-Circuit Voltage Using a Stacking Structure
(2010-12-01)
Chang, Tzu-Yueh
;
Chang, Chun-Lung
;
Lee, Hsin-Yu
;
Lee, Po-Tsung
;
光電工程學系
A stacking metal-insulator-semiconductor (MIS) solar cell structure, which integrates an n-type MIS solar cell with a p-type MIS solar cell, is proposed to effectively enlarge the open-circuit voltage (V(oc)). The measured V(oc) is up to 0.71 V under simulated air mass 1.5 illumination (100 mW/cm(2)). This V(oc) is larger than those of the n-type or p-type MIS solar cells with or without surface passivation. In this letter, we successfully demonstrate the feasibility of the V(oc) enhancement of MIS solar cells by using a stacking structure.
A recurrent fuzzy coupled cellular neural network system with automatic structure and template learning
(2006-08-01)
Chang, Chun-Lung
;
Fan, Kan-Wei
;
Chung, I-Fang
;
Lin, Chin-Teng
;
電控工程研究所
The cellular neural network (CNN) is a powerful technique to mimic the local function of biological neural circuits, especially the human visual pathway system, for real-time image and video processing. Recently, many studies show that an integrated CNN system can solve more complex high-level intelligent problems. In this brief, we extend our previously proposed multi-CNN integrated system, called recurrent fuzzy CNN (RFCNN) which considers uncoupled CNNs only, to automatically learn the proper network structure and parameters simultaneously of coupled CNNs, which is called recurrent fuzzy coupled CNN (RFCCNN). The proposed RFCCNN provides a solution to the current dilemma on the decision of templates and/or fuzzy rules in the existing integrated (fuzzy) CNN systems. For comparison, the capability of the proposed RFCCNN is demonstrated on the same defect inspection problems. Simulation results show that the proposed RFCCNN outperforms the RFCNN.
Characteristics of MIS Solar Cells Using Sputtering SiO(2) Insulating Layers
(2010-01-01)
Chang, Tzu-Yueh
;
Chang, Chun-Lung
;
Lee, Hsin-Yu
;
Lee, Po-Tsung
;
光電工程學系
In this study, the characteristics of both p-type and n-ype Metal-Insulator-Semiconductor (MIS) solar cells with sputtering SiO(2) insulating layers fabricated by radio-frequency (RF) magnetron sputtering are investigated. The characteristics of MIS solar cells are considerably affected by the thickness of the SiO(2) insulating layer and a hydrogen (H(2)) annealing process. Moreover, the performance of MIS solar cells with sputtering SiO(2) insulating layer can be greatly enhanced by depositing a SiN(x) passivation layer on top. It suggests that a sputtering SiO(2) insulating layer is a good alternative insulating layer for MIS solar cells.
Characteristics of MIS Solar Cells Using Sputtering SiO2 Insulating Layers
(2010-01-01)
Chang, Tzu-Yueh
;
Chang, Chun-Lung
;
Lee, Hsin-Yu
;
Lee, Po-Tsung
;
光電工程學系
In this study, the characteristics of both p-type and n-ype Metal-Insulator-Semiconductor (MIS) solar cells with sputtering SiO2 insulating layers fabricated by radio-frequency (RF) magnetron sputtering are investigated. The characteristics of MIS solar cells are considerably affected by the thickness of the SiO2 insulating layer and a hydrogen (H-2) annealing process. Moreover, the performance of MIS solar cells with sputtering SiO2 insulating layer can be greatly enhanced by depositing a SiNx passivation layer on top. It suggests that a sputtering SiO2 insulating layer is a good alternative insulating layer for MIS solar cells.
Dynamic system identification using a recurrent compensatory fuzzy neural network
(2008-10-01)
Lee, Chi-Yung
;
Lin, Cheng-Jian
;
Chen, Cheng-Hung
;
Chang, Chun-Lung
;
電控工程研究所
This Study presents a recurrent compensatory fuzzy neural network (RCFNN) for dynamic system identification. The proposed RCFNN uses a compensatory fuzzy reasoning method, and has feedback connections added to the rule layer of the RCFNN. The compensatory fuzzy reasoning method can make the fuzzy logic system more effective, and the additional feedback connections can solve temporal problems as well. Moreover, an online learning algorithm is demonstrated to automatically construct the RCFNN. The RCFNN initially contains no rules. The rules are created and adapted as online learning proceeds via simultaneous structure and parameter learning. Structure learning is based on the measure of degree and parameter learning is based on the gradient descent algorithm. The simulation results from identifying dynamic systems demonstrate that the convergence speed of the proposed method exceeds that of conventional methods. Moreover, the number of adjustable parameters of the proposed method is less than the other recurrent methods.