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최대 10 단어까지만 선택 가능합니다.
최대 10 단어까지만 선택 가능합니다.
제안 방법
- 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.
- 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.
- 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 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.
- 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°.
- 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.
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