Dependence of decamethylcyclopentasiloxane （DMCPS） organosilicon dissociation on ionized energy in the energy range of 25 eV to 70 eV is investigated by using a quadrupole mass spectrometry. At the ionized energy be...Dependence of decamethylcyclopentasiloxane （DMCPS） organosilicon dissociation on ionized energy in the energy range of 25 eV to 70 eV is investigated by using a quadrupole mass spectrometry. At the ionized energy below 55 eV, the dissociation of DMCPS is dominant. As the ionized energy is above 55 eV, the DMCPS dissociation achieves the maximum cross section, while the fragments from the DMCPS dissociation can further dissociate, which leads to a different ingredient of fragments. At the lower ionized energy of 25 eV, the main fragments are SiOC2H＋, SiCH＋, Si＋, O＋ and CH＋ ions, which shows an important effect on the SiCOH low-k film deposition.展开更多
This paper investigated the radical behaviour of the plasma of a mixture of methane （CH4） and decamethylcyclopentasiloxane （DMCPS） by optical emission spectroscopy. The plasma was generated by electron cyclotron r...This paper investigated the radical behaviour of the plasma of a mixture of methane （CH4） and decamethylcyclopentasiloxane （DMCPS） by optical emission spectroscopy. The plasma was generated by electron cyclotron resonance （ECR） discharge and was used for depositing porous SiCOH low dielectric-constant film. In the ECR discharge plasma, CH, H, H2, C2, Si, O and SiO radicals were obtained. The CH, H and C2 radicals were from the dissociation of CH4, while the SiO. Si and O radicals from the dissociation of the Si-O chain. CHx radicals absorbed in the film were thermally unstable and could be removed by annealing. The dissociation of the Si-O chain led to an increase in a ratio of the Si-Ocage to Si-Onetwork. The removed of CHx radicals and the increased Si-Ocage to Si-Onetwork ratio were beneficial for reducing the film density and dielectric constant.展开更多
Being progressively applied in the design of highly active catalysts for energy devices,machine learning(ML)technology has shown attractive ability of dramatically reducing the computational cost of the traditional de...Being progressively applied in the design of highly active catalysts for energy devices,machine learning(ML)technology has shown attractive ability of dramatically reducing the computational cost of the traditional density functional theory(DFT)method,showing a particular advantage for the simulation of intricate system catalysis.Starting with a basic description of the whole workflow of the novel DFT-based and ML-accelerated(DFT-ML)scheme,and the common algorithms useable for machine learning,we presented in this paper our work on the development and performance test of a DFT-based ML method for catalysis program(DMCP)to implement the DFT-ML scheme.DMCP is an efficient and user-friendly program with the flexibility to accommodate the needs of performing ML calculations based on the data generated by DFT calculations or from materials database.We also employed an example of transition metal phthalocyanine double-atom catalysts as electrocatalysts for carbon reduction reaction to exhibit the general workflow of the DFT-ML hybrid scheme and our DMCP program.展开更多
文摘Dependence of decamethylcyclopentasiloxane （DMCPS） organosilicon dissociation on ionized energy in the energy range of 25 eV to 70 eV is investigated by using a quadrupole mass spectrometry. At the ionized energy below 55 eV, the dissociation of DMCPS is dominant. As the ionized energy is above 55 eV, the DMCPS dissociation achieves the maximum cross section, while the fragments from the DMCPS dissociation can further dissociate, which leads to a different ingredient of fragments. At the lower ionized energy of 25 eV, the main fragments are SiOC2H＋, SiCH＋, Si＋, O＋ and CH＋ ions, which shows an important effect on the SiCOH low-k film deposition.
基金National Natural Science Foundation of China(Nos.10575074,10635010)
文摘This paper investigated the radical behaviour of the plasma of a mixture of methane （CH4） and decamethylcyclopentasiloxane （DMCPS） by optical emission spectroscopy. The plasma was generated by electron cyclotron resonance （ECR） discharge and was used for depositing porous SiCOH low dielectric-constant film. In the ECR discharge plasma, CH, H, H2, C2, Si, O and SiO radicals were obtained. The CH, H and C2 radicals were from the dissociation of CH4, while the SiO. Si and O radicals from the dissociation of the Si-O chain. CHx radicals absorbed in the film were thermally unstable and could be removed by annealing. The dissociation of the Si-O chain led to an increase in a ratio of the Si-Ocage to Si-Onetwork. The removed of CHx radicals and the increased Si-Ocage to Si-Onetwork ratio were beneficial for reducing the film density and dielectric constant.
文摘Being progressively applied in the design of highly active catalysts for energy devices,machine learning(ML)technology has shown attractive ability of dramatically reducing the computational cost of the traditional density functional theory(DFT)method,showing a particular advantage for the simulation of intricate system catalysis.Starting with a basic description of the whole workflow of the novel DFT-based and ML-accelerated(DFT-ML)scheme,and the common algorithms useable for machine learning,we presented in this paper our work on the development and performance test of a DFT-based ML method for catalysis program(DMCP)to implement the DFT-ML scheme.DMCP is an efficient and user-friendly program with the flexibility to accommodate the needs of performing ML calculations based on the data generated by DFT calculations or from materials database.We also employed an example of transition metal phthalocyanine double-atom catalysts as electrocatalysts for carbon reduction reaction to exhibit the general workflow of the DFT-ML hybrid scheme and our DMCP program.
