Browsing by Author "Wang, JK"

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Integration of MOCVD titanium nitride with collimated titanium and ion metal plasma titanium for 0.18-mu m logic process
(2001-11-01)
Lan, JK
;
Wang, YL
;
Lo, KY
;
Liu, CP
;
Liu, CW
Both collimated titanium (CO-Ti) and ion metal plasma titanium (IMP-Ti) have been widely used for plug liner layers. The focused ion beam (FIB) images of these films show that the IMP-Ti surface is granular and the CO-Ti surface is amorphous. It is concluded that the lower reflectivity and resistivity of IMP-Ti films are caused by the fact that IMP-Ti has a larger grain than that of the CO-Ti films. We define a grain size factor (GF) and find that the activation energy of grain growth of IMP-Ti film is 3.8 times larger than that of CO-Ti film. The electric measurement of vias resistance shows that IMP-Ti plug liner gives 13.6% higher via resistance and broader distribution than that of the CO-Ti plug liner. From the experimental data, we can conclude that there are three factors that make the IMP Ti plug liner process have higher vias resistance. These three factors are IMP-Ti resistivity, IMP-Ti thickness and CVD TiN thickness. (C) 2001 Elsevier Science B.V. All rights reserved.
Robot motion similarity analysis using an FNN learning mechanism
(2001-12-01)
Young, KY
;
Wang, JK
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Learning controllers are usually subordinate to conventional controllers in governing multiple-joint robot motion, in spite of their ability to generalize, because learning space complexity and motion variety require them to consume excessive amount of memory when they are employed as major roles in motion governing. We propose using a fuzzy neural network (FNN) to learn and analyze robot motions so that they can be classified according to similarity. After classification, the learning controller can then be designed to govern robot motions according to their similarities without consuming excessive memory resources. (C) 2001 Elsevier Science B.V. All rights reserved.
Similarity analysis for robot motions using an FNN learning mechanism
(1997)
Young, KY
;
Wang, JK
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Learning controllers are usually subordinate to conventional controllers in governing multiple-joint robot motion, in spite of their ability to generalize, because learning-space complexity and motion variety require them to consume exccessive amount of memory. We propose using a Fuzzy Neural Network (FNN) to learn and analyze robot motions so they can be classified according to similarity. After classification, the learning controller can then be designed to govern robot motions according to their similarities without consuming excessive memory resources.
Study the impact of liner thickness on the 0.18 mu m devices using low dielectric constant hydrogen silsesquioxane as the interlayer dielectric
(2000-12-01)
Lan, JK
;
Wang, YL
;
Wu, YL
;
Liou, HC
;
Wang, JK
The electrical performance of hydrogen silsesquioxane (HSQ) as the interlayer level dielectric (ILD) has been determined by using two-metal-layered test structures to study the impact of oxide liner thickness on the capacitance reduction. In comparison with SiO2, HSQ test structures formed with SiO2 cap and liner or with SiO2 cap only, have 20-27% lower intraline capacitance while 6-16% reduction was observed for fluorosilicate glass (FSG) relative to SiO2. It was found that the capacitance of SiO2/HSQ ILDs did not vary with oxide liner thickness as expected. Similar effects were observed with via resistance measurement. Analysis of the structure shows that wide variation of SiO2/HSQ/SiO2 stack thickness after oxide Chemical Mechanical Polishing (CMP) step changed the expected contribution of liner thickness on the intraline and interlayer capacitance. This thickness variation also has a strong impact on landed/unlanded via resistance. Therefore, a good control of oxide CMP on the ILD stack is needed to reduce the thickness variation of the liner/HSQ/cap ILD stack which in turn will enhance process yields in the 0.18 mum devices. (C) 2000 Elsevier Science B.V. All rights reserved.