Browse > Article
http://dx.doi.org/10.9725/kts.2022.38.4.152

Nonuniformity of Conditioning Density According to CMP Conditioning System Design Variables Using Artificial Neural Network  

Park, Byeonghun (Graduate School, Dept. of Mechanical Engineering, Dong-A University)
Lee, Hyunseop (Dept. of Mechanical Engineering, Dong-A University)
Publication Information
Tribology and Lubricants / v.38, no.4, 2022 , pp. 152-161 More about this Journal
Abstract
Chemical mechanical planarization (CMP) is a technology that planarizes the surfaces of semiconductor devices using chemical reaction and mechanical material removal, and it is an essential process in manufacturing highly integrated semiconductors. In the CMP process, a conditioning process using a diamond conditioner is applied to remove by-products generated during processing and ensure the surface roughness of the CMP pad. In previous studies, prediction of pad wear by CMP conditioning has depended on numerical analysis studies based on mathematical simulation. In this study, using an artificial neural network, the ratio of conditioner coverage to the distance between centers in the conditioning system is input, and the average conditioning density, standard deviation, nonuniformity (NU), and conditioning density distribution are trained as targets. The result of training seems to predict the target data well, although the average conditioning density, standard deviation, and NU in the contact area of wafer and pad and all areas of the pad have some errors. In addition, in the case of NU, the prediction calculated from the training results of the average conditioning density and standard deviation can reduce the error of training compared with the results predicted through training. The results of training on the conditioning density profile generally follow the target data well, confirming that the shape of the conditioning density profile can be predicted.
Keywords
Chemical mechanical planarization; Pad conditioning; Design ariables; Conditioning simulation; Artificial neural network;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Lv, C., Xing, Y., Zhang, J., Na, X., Li, Y., Liu, T., "Levenberg-Marquardt Backpropagation Training of Multilayer Neural Networks for State Estimation of A Safety Critical Cyber-Physical System," IEEE Trans. Industr. Infrm., Vol.14, Issue8, pp.3436-3446, 2017, https://doi.org/10.1109/TII.2017.2777460   DOI
2 Lee, H., Kim, H., Jeong, H., "Approaches to Sustainability in Chemical Mechanical Polishing (CMP): A Review," Int. J. Precis. Eng. Manufact.-Green Technology, Vol.9, Issue1, pp.349-367, 2022, https://doi.org/10.1007/s40684-021-00406-8   DOI
3 Lee, H., Lee, D., Jeong, H., "Mechanical aspects of the chemical mechanical polishing process: A review", Int. J. Precis. Eng. Manufact., Vol.17, No.4, pp.525- 536, 2016, https://doi.org/10.1007/s12541-016-0066-0   DOI
4 Son, J., Lee, H., "Contact-Area-Changeable CMP Conditioning for Enhancing Pad Lifetime," Appl. Sci., Vol.11, pp.3521, 2021, https://doi.org/10.3390/app11083521   DOI
5 Zhou, Y. -Y, Davis, E. C., "Variation of polish pad shape during pad dressing," Mater. Sci. Eng. B, Vol.68, Issue2, pp.91-98, 1999, https://doi.org/10.1016/S0921-5107(99)00423-7   DOI
6 Chang, O., Kim, H., Park, K., Park, B., Seo, H., Jeong, H., "Mathematical modeling of CMP conditioning process," Micronelectron. Eng., Vol.84, Issue4, pp.577-583, 2007, https://doi.org/10.1016/j.mee.2006.11.011   DOI
7 Lee, H., Lee, S., "Investigation of pad wear in CMP with swing-arm conditioning and uniformity of material removal," Precis. Eng., Vol.49, pp.85-91, 2017, https://doi.org/10.1016/j.precisioneng.2017.01.015   DOI
8 Park, B., Park, B., Jeon, U., Lee, H., "Design Variables of Chemical-Mechanical Polishing Conditioning System to Improve Pad Wear Uniformity," Tribol. Lubr., Vol.38, No.1, pp.1-7, 2022, https://doi.org/10.9725/kts.2022.38.1.1   DOI
9 Li, K., Li, S., Fan, C., Cao, Y., Li, L., "The Application of BP Neural Network in Students' Evaluation," Topics in Chem. Mater. Eng., Vol.1, No.1, pp.463-465, 2018, http://doi.org/10.26480/icnmim.01.2018.463.465   DOI
10 Marquardt, D. W., "An Algorithm for Least-Squares Estimation of Nonlinear Parameters," J. Soc. Indust., Appl. Math., Vol.11, No.2, pp.431-441, 1963, https://doi.org/10.1137/0111030   DOI
11 Zhao, D., Lu, X., "Chemical Mechanical Polishing: Theory and experiment," Friction, Vol.1, Issue4, pp.306-326, 2013, https://doi.org/10.1007/s40544-013-0035-x   DOI
12 Li, Z. C., Baisie, E. A., Zhang, X. H., "Diamond disc pad conditioning in chemical mechanical planarization (CMP): A surface element method to predict pad surface shape," Precis. Eng., Vol.36, pp.356-363, 2012, https://doi.org/10.1016/j.precisioneng.2011.10.006   DOI
13 Lee, S., Jeong, S., Park,. K., Kim, H., Jeong, H., "Kinematical modeling of pad profile variation during conditioning in chemical mechanical polishing," Jpn. J.. Appl. Phys., Vol.48, No.12R, pp.126502, 2009, https://doi.org/10.1143/JJAP.48.126502   DOI