Impact of Pad Conditioning on Thickness Profile Control in Chemical Mechanical Planarization


KINCAL S., BAŞIM DOĞAN G. B.

JOURNAL OF ELECTRONIC MATERIALS, cilt.42, sa.1, ss.83-96, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 42 Sayı: 1
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1007/s11664-012-2250-z
  • Dergi Adı: JOURNAL OF ELECTRONIC MATERIALS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.83-96
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Chemical mechanical planarization (CMP) has been proven to be the best method to achieve within-wafer and within-die uniformity for multilevel metallization. Decreasing device dimensions and increasing wafer sizes continuously demand better planarization, which necessitates better understanding of all the variables of the CMP process. A recently highlighted critical factor, pad conditioning, affects the pad surface profile and consequently the wafer profile; in addition, it reduces defects by refreshing the pad surface during polishing. This work demonstrates the changes in the postpolish wafer profile as a function of pad wear. It also introduces a wafer material removal rate profile model based on the locally relevant Preston equation by estimating the pad thickness profile as a function of polishing time. The result is a dynamic predictor of how the wafer removal rate profile shifts as the pad ages. The model helps fine-tune the pad conditioner operating characteristics without the requirement for costly and lengthy experiments. The accuracy of the model is demonstrated by experiments as well as data from a real production line. Both experimental data and simulations indicate that the smaller conditioning disk size and extended conditioning sweep range help improve the post-CMP wafer planarization. However, the defectivity tends to increase when the conditioning disk sweeps out of the pad radius; hence, the pad conditioning needs to be designed by considering the specific requirements of the CMP process conducted. The presented model predicts the process outcomes without requiring detailed experimentation.