Browsing by Author "Lee, Yung-Chou"

Now showing 1 - 3 of 3

Chemical mechanical planarization operation via dynamic programming
(2007-12-01)
Lin, Chin-Shui
;
Lee, Yung-Chou
;
機械工程學系
;
電控工程研究所
;
Department of Mechanical Engineering
In this paper, the impact on non-planarization index by the down force and rotational speed during a SiO2 or Cu CMP process was investigated. Since the magnitudes of down force and rotational speed have limits, we choose the dynamic programming approach because of its ability to achieve constrained optimization by the down force and rotational speed. The duration and the amount of input were computed based on the chemical mechanical polishing model by Luo and Dornfeld [J. Luo, D.A. Dornfeld, IEEE Trans. Semiconduct. Manufact. 14(2) (2001) 112-132.] when the other parameters were fixed. Experiments done for blanket wafers based on dynamic programming operation and conventional constant removal rate operation was compared with each other. The non-planarization index could be improved consistently by dynamic programming operation versus constant removal rate operation. The improvement ranges from 2% to 39% improvement over the base recipe of constant removal rate in all experiments as shown in Tables 3 and 6. The thickness removal error is consistently smaller by constant removal rate operation versus dynamic programming operation in all experiments as shown in Tables 3 and 6. To get the best performance of both planarization and thickness removal, it is recommended that planarization step and overpolish step in SiO2 and Cu CMP should use different mode of operation, i.e., dynamic programming operation during planarization step for minimizing non-planarization index and constant removal rate operation during overpolish step for minimizing thickness removal error. The incremental time calculation for eliminating thickness removal error during overpolish step can be done using the thickness error and removal rate derived from Luos' removal rate model based on constant wafer pressure and platen speed at the end of planarization step. Our contribution is a new approach for CMP. Standard CMP uses constant removal rate operation in both planarization step and overpolish step. Our new approach uses dynamic programming operation during planarization step and constant removal rate operation during overpolish step. (C) 2007 Elsevier B.V. All rights reserved.
Clustering of Laser Measurements via the Dirichlet Process Mixture Model for Object Tracking
(2012)
Lee, Yung-Chou
;
Hsiao, Tesheng
;
Chang, Chih-Tang
;
交大名義發表
;
National Chiao Tung University
In this paper, the Dirichlet process mixture model is used to describe the distribution of the whole laser measurements in a given scan. Then the number of clusters is inferred from the measurements by the Gibbs sampler. We focus on the automotive application which usually has a more complex environment. Due to the variant shapes and sizes of the real traffic objects, the multi-class DP-based clustering model, which is incorporated with a mixture prior distribution, is proposed to cluster the measurements more properly. The clustering results of the proposed method are compared with those of several existing clustering methods both in an expressway case and in an urban road case. The corresponding tracking performances are also analyzed and the improvements of the proposed method are presented.
Object Tracking via the Probability-Based Segmentation Using Laser Range Images
(2010)
Lee, Yung-Chou
;
Hsiao, Tesheng
;
交大名義發表
;
National Chiao Tung University
In this paper, a probability-based segmentation approach is presented for object tracking. The proposed approach uses the Dirichlet process mixture model to describe the probabilistic distribution of observations in a single scan of a laserscanner. Then the number of segments is inferred from the observations by the Gibbs sampling method. Moreover each segment is classified into one of the three predefined classes such that most of non-vehicle-like objects on the roadsides can be filtered out. Then, the tracking algorithm, called Joint Integrated Probabilistic Data Association Filter (JIPDAF), is applied to track the classified objects and manage existing tracks. Simulations based on real traffic data demonstrate that the non-vehicle-like objects on the roadsides are suppressed. Since the number of objects in the tracking step is decreased, the computation load of the tracking step is decreased.