Junkyo Suh | Searcharxiv

arXiv2021

Low Resistance III-V Hetero-contacts to N-Ge

We experimentally study III-V/Ge heterostructure and demonstrate InGaAs hetero-contacts to n-Ge with a wide range of In % and achieve low contact resistivity ($ρ_C$) of $5\times10^{-8} Ω\cdot cm^2$ for Ge doping of $3 \times 10^{19} cm^{-3}$. This results from re-directing the charge neutrality level (CNL) near the conduction band and benefiting from low effective mass for high electron transmission. For the first time, we observe that the heterointerface presents no temperature dependence despite the two different conduction minimum valley locations of III-V ($Γ$-valley) and Ge (L-valley), which potentially stems from elastic trap-assisted tunneling through defect states at the interface generated by dislocations. The hetero-interface plays a dominant role in the overall $ρ_C$ below $\approx 1 \times 10^{-7} Ω\cdot cm^2$, which can be further improved with large active dopant concentration in Ge by co-doping.

cond-mat.mtrl-sci↗

arXiv2018

Neural Stain Normalization and Unsupervised Classification of Cell Nuclei in Histopathological Breast Cancer Images

In this paper, we develop a complete pipeline for stain normalization, segmentation, and classification of nuclei in hematoxylin and eosin (H&E) stained breast cancer histopathology images. In the first step, we use a CNN-based stain transfer technique to normalize the staining characteristics of (H&E) images. We then train a neural network to segment images of nuclei from the H&E images. Finally, we train an Information Maximizing Generative Adversarial Network (InfoGAN) to learn visual representations of different types of nuclei and classify them in an entirely unsupervised manner. The results show that our proposed CNN stain normalization yields improved visual similarity and cell segmentation performance compared to the conventional SVD-based stain normalization method. In the final step of our pipeline, we demonstrate the ability to perform fully unsupervised clustering of various breast histopathology cell types based on morphological and color attributes. In addition, we quantitatively evaluate our neural network - based techniques against various quantitative metrics to validate the effectiveness of our pipeline.

cs.CV↗