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[국내논문] Characterization of via etch by enhanced reactive ion etching 원문보기

한국결정성장학회지 = Journal of the Korean crystal growth and crystal technology, v.14 no.6, 2004년, pp.236 - 243  

Bae, Y.G. (Department of Electronic Engineering, Hanseo University) ,  Park, C.S. (Department of Electronic Engineering, Hanseo University)

Abstract ▼ AI-Helper 아이콘AI-Helper

The oxide etching process was characterized in a magnetically enhanced reactive ion etching (MERIE) reactor with a $CHF_3CF_4$ gas chemistry. A statistical experimental design plus one center point was used to characterize relationships between process factors and etch response. The etch ...

주제어

#Oxide etching   #Uniformity   #Via etching   #Etching mechanism  

AI 본문요약
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제안 방법

  • Ib get the knowledge about reaction mechanism related to CF4/CHF3 gas composition, active species to be gen­ erated in the plasma and etch residues on etched SiO2 surface as a ftmction of gas composition were analyzed by using optical emission spectroscopy (OES) and x-ray photoelectron spectroscopy (XPS), respectively. XPS (ESCALAB 200R_VG Scientific) analyses have been performed by collecting Cis and FIs regions at pass energies of 20 eV with AlKa x-ray source at take-off angles of 90°.
  • In this study, neural network is used to model character­ istics of oxide film etched in CHF3/CF4/Ar gas chemistry. A magnetically enhanced reactive ion etcher (MERIE) was used for etching.
  • The output layer transmits the data and thus corresponds to the various plasma attributes (electron density, electron temperature, and plasma potential). In this study, the number of neurons in the outp니t layer was set to unity since each attribute was modeled one by one. BPNN also incorporates "hidden" layers of neu­ rons that do not interact with the outside world, but assists in performing nonlinear feature extraction on the data provided by the input and output layers.
  • respectively. Prior to this work, we examined the etching rate of TiN films with various CF4 gas flow rates and found out the increase of TiN etch rate with increasing CF4 gas flow rates. The report described that TiFx (x = 3~4) was etching by-products and the forma­ tion of TiFx (x = 3~4) depended on F radical density.
  • Experimental ranges of fac­ tors are contained in Table 1. Resultant 9 experiments were used to train neural networks and trained net­ works were tested on 8 experiments were additionally conducted and thus a total of 17 experiments were per­ formed to develop a predictive etch model.
  • The etch responses modeled are etch rate, etch selectivity to TiN, and uni­ formity. The developed models were then utilized to produce 3D response plots.
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참고문헌 (9)

  1. R.J. Schutz, in VLSI Technology, 2nd ed., edited by S.M. Sze (McGraw-Hill, New York, 1988) 

  2. J.M. Cook and K.G. Donohoe, 'Etching issues at 0.35 $\mu$ n and below', Solid State Technolog 34 (1991) 119. 

  3. B. Kim, J. Sun, C. Choi, D. Lee and Y. Seol, 'Use of neural networks to model low-temperature tungsten etch characteristics in high density $SF_6$ plasma', J. Vac. Sci. Technol. A18 (2000) 417 

    상세보기 crossref

  4. C.D. Himmel and G.S. May, 'Advantages of plasma etch modeling using neural networks over statistical techniques', IEEE Trans. Semicond. Manufact. 6 (1993) 103 

    상세보기 crossref

  5. B. Kim and G.T. Park, 'Modeling plasma equipment using neural networks', IEEE Trans Plasma Sci. 29(2001) 8 

    상세보기 crossref

  6. S.H. Oh and S.Y. Lee, 'An adaptive learning rate with limited error signals for training of multilayer perceptrons', ETRI Journal 22(4) (2000) 40 

    원문보기 상세보기 crossref

  7. D.C. Mongomery, Design and Analysis of Experiments (John Wiley & Sons, 1991) 

  8. D.E. Rummelhart and J.L. McClelland, Parallel Distributed Processing (Cambridge, M.LT. Press, 1986) 

  9. B. Kim and G.S. May, 'An optimal neural network process model for plasma etching', IEEE Trans. Semicondo Manufact. 7 (1994) 12 

    상세보기 crossref

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