The inductively coupled plasma-reactive ion etching (ICP-RIE) of SiC single crystals using the C 2 F 6 /O 2 gas mixture was investigated. It was observed that the etch rate increased as the ICP power and bias power increased. With increasing sample-coil distance, O 2 concentration, and chamber pressure, the etch rate initially increased, reached a maximum, and then decreased. Mesas with smooth surfaces (roughness Յ 1 nm) and vertical sidewalls (ϳ85°) were obtained at low bias conditions with a reasonable etch rate of about 100 nm/min. A maximum etch rate of 300 nm/min could be obtained by etching at high bias conditions (Ն300 V), in which case rough surfaces and the trenched sidewall base were observed. The trenching effect could be suppressed by etching the samples on anodized Al plates, although mesas with sloped (60-70°) sidewalls were obtained. Results of various surface characterization indicated little contamination and damage on the etched SiC surfaces. 210Kong, Choi, Lee, Han, and Lee Fig. 1. Etch characteristics of 6H-SiC and 4H-SiC as a function of (a) ICP source power, (b) bias power, (c) pressure, (d) O 2 percentage in C 2 F 6 /O 2 , and (e) the distance between the substrate holder and the source coil.

Clays in fault zones have low electrical resistivity, making electrical resistivity tomography (ERT) effective for fault investigations. However, traditional ERT inversion methods struggle to find a unique solution and produce unstable results owing to the ill-posed nature of the problem. To address this, a workflow integrating deep-learning (DL) technology with traditional ERT inversion is proposed. First, a deep-learning model named DL-ERT inversion that maps apparent resistivity data to subsurface resistivity models is developed. To create target-oriented training data, we use approximately 150 field borehole data acquired from various survey areas in South Korea. The DL-ERT inversion algorithm is based on a U-Net structure and includes an additional network called the borehole mixer to incorporate borehole information when available. The DL-ERT inversion model is trained in three stages: base model training, borehole mixer training, and fine-tuning. Results showed that the fine-tuning model provided the highest prediction accuracy for all test datasets. Next, the prediction of the trained model is used as the initial model for the deterministic inversion method to predict the final subsurface model. The efficiency and accuracy of the proposed workflow are demonstrated in fault detection using a field data example compared with traditional deterministic inversion.

Silicon carbide (SiC) has been etched in a C2F6/O2 inductively coupled plasma and modeled using neural networks. A 25 full factorial experiment was used to characterize the relationships between input process factors and etch response. The factors that were varied include source power, bias power, pressure, O2 fraction, and gap between the chuck holder and coil antenna. Neural networks were trained on the resultant 32 experiments and then tested on 18 additional experiments to evaluate prediction accuracy. Due to little variations in etch anisotropy, etch rate was only modeled and its root-mean-squared prediction error was 23.9 nm/min. Etch rate was found to be a strong function of source power. Increasing etch rate with pressure may partly be attributed to increased ion density and ion energy. Placing the chuck holder closer to the source antenna coil increased the etch rate. At higher bias powers, increasing the O2 fraction resulted in a crossover. This crossover seems to be weakened significantly with a decrease in bias power. Although etch anisotropy did not vary consistently with source power, it improved consistently with bias power. Microtrenches were noticed for variations in each of the five factors. With increasing pressure, the anisotropy was slightly degraded while being insensitive to a variation in the gap.