Login / Signup

Selective Digital Etching of Silicon-Germanium Using Nitric and Hydrofluoric Acids.

Chen LiHuilong ZhuYongkui ZhangXiaogen YinKunpeng JiaJunjie LiGuilei WangZhenzhen KongAnyan DuTengzhi YangLiheng ZhaoWeixing HuangLu XieYangyang LiXuezheng AiShishuai MaHenry H Radamson
Published in: ACS applied materials & interfaces (2020)
A digital etching method was proposed to achieve excellent control of etching depth. The digital etching characteristics of p+-Si and Si0.7Ge0.3 using a combination of HNO3 oxidation and buffered oxide etching oxide removal processes were investigated. Experimental results showed that oxidation saturates as time goes on because of low activation energy and its diffusion-limited characteristic. An oxidation model was developed to describe the wet oxidation process with nitric acid. The model was calibrated with experimental data, and the oxidation saturation time, final oxide thickness, and selectivity between Si0.7Ge0.3 and p+-Si were obtained. In Si0.7Ge0.3/p+-Si stacks, the saturated relative etched depth per cycle was 0.5 nm (four monolayers), and variation between experiments was about 4% after saturation. A corrected selectivity calculation formula was also proposed, and the calculated selectivity was 3.7-7.7 for different oxidation times, which was the same as the selectivity obtained from our oxidation model. The proposed model can be used to analyze process variations and repeatability, and it can provide credible guidance for the design of other wet digital etching experiments.
Keyphrases
  • hydrogen peroxide
  • room temperature
  • electron transfer
  • optical coherence tomography
  • visible light
  • machine learning
  • photodynamic therapy
  • nitric oxide
  • electronic health record
  • deep learning
  • high resolution