Browsing by Author "Wu, Y"

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Effect of carrier gas on the structure and electrical properties of low dielectric constant SiCOH film using trimethylsilane prepared by plasma enhanced chemical vapor deposition
(2004-12-22)
Cheng, YL
;
Wang, Y
;
Lan, JK
;
Chen, HC
;
Lin, JH
For a low dielectric constant inter-metal dielectric application, the low-k SiCOH film with a dielectric constant of 2.8-3.2 has been deposited by plasma-enhanced chemical vapor deposition (PE-CVD) with trimethylsilane (3MS), oxygen, and the carrier gas. In this work, we present the effects of the carrier gas on the characterizations of the low-k films, including reaction mechanism, the deposition rate, and the mechanical and electrical properties. According to the experimental results, the low-k film in Ar carrier gas exhibits the improvement in deposition rate, nonuniformity, leakage current, and hardness. In addition, the dielectric constant of the deposited low-k film is slightly sacrificed due to the decrease of micropores in the deposited films. (C) 2004 Elsevier B.V. All rights reserved.
Learnable self-feedback ratio-memory cellular nonlinear network (SRMCNN) for associative memory applications
(2004)
Lai, JL
;
Wu, Y
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
A self-feedback ratio-memory cellular nonlinear network (SRMCNN) with the B template and the modified Hebbian learning algorithm to learn and recognize the image patterns is proposed and analyzed. In the proposed SRMCNN, the coefficients of space-variant B templates are determined from the exemplar patterns during the learning period. The weights are the ratio of the absolute summation of its neighborhood weights in the B templates was stored in the associative memory. This SRMCNN can recognize the learned patterns with distinct white-black noise and output the correct patterns. The Matlab and HSPICE software has been simulated the operation of the proposed SRMCNN. It is shown that the 18x18 SRMCNN can successfully learned and recognized 8 incompletely noisy patterns. As compared to other learnable CNN as associate memories, the proposed SRMCNN could improve pattern learning and recognition capability. The architecture can be implemented in nano-CMOS technology for giga-scale learning system in the real-time applications.