arXiv subjects

Jisoo Kim

Publications and source records attributed to Jisoo Kim.

40 records · Page 3Linked to original sources

arXiv2021

Influences of ALD Al$_2$O$_3$ on the surface band-bending of c-plane, Ga-face GaN and the implication to GaN-collector npn heterojunction bipolar transistors

Due to the lack of effective p-type doping in GaN and the adverse effects of surface band-bending of GaN on electron transport, developing practical GaN heterojunction bipolar transistors has been impossible. The recently demonstrated approach of grafting n-type GaN with p-type semiconductors, like Si and GaAs, by employing ultrathin (UO) Al$_2$O$_3$ at the interface of Si/GaN and GaAs/GaN, has shown the feasibility to overcome the poor p-type doping challenge of GaN by providing epitaxy-like interface quality. However, the surface band-bending of GaN that could be influenced by the UO Al2O3 has been unknown. In this work, the band-bending of c-plane, Ga-face GaN with UO Al2O3 deposition at the surface of GaN was studied using X-ray photoelectron spectroscopy (XPS). The study shows that the UO Al2O3 can help in suppressing the upward band-bending of the c-plane, Ga-face GaN with a monotonic reduction trend of the upward band-bending energy from 0.48 eV down to 0.12 eV as the number of UO Al2O3 deposition cycles is increased from 0 to 20 cycles. The study further shows that the band-bending can be mostly recovered after removing the Al2O3 layer, concurring that the change in the density of fixed charge at the GaN surface caused by UO Al2O3 is the main reason for the surface band-bending modulation. The potential implication of the surface band-bending results of AlGaAs/GaAs/GaN npn heterojunction bipolar transistor (HBT) was preliminarily studied via Silvaco(R) simulations.

physics.app-ph↗

arXiv2019

Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training

Multi-scale (MS) approaches have been widely investigated for blind single image / video deblurring that sequentially recovers deblurred images in low spatial scale first and then in high spatial scale later with the output of lower scales. MS approaches have been effective especially for severe blurs induced by large motions in high spatial scale since those can be seen as small blurs in low spatial scale. In this work, we investigate alternative approach to MS, called multi-temporal (MT) approach, for non-uniform single image deblurring. We propose incremental temporal training with constructed MT level dataset from time-resolved dataset, develop novel MT-RNNs with recurrent feature maps, and investigate progressive single image deblurring over iterations. Our proposed MT methods outperform state-of-the-art MS methods on the GoPro dataset in PSNR with the smallest number of parameters.

eess.IV↗

arXiv2019

Down-Scaling with Learned Kernels in Multi-Scale Deep Neural Networks for Non-Uniform Single Image Deblurring

Multi-scale approach has been used for blind image / video deblurring problems to yield excellent performance for both conventional and recent deep-learning-based state-of-the-art methods. Bicubic down-sampling is a typical choice for multi-scale approach to reduce spatial dimension after filtering with a fixed kernel. However, this fixed kernel may be sub-optimal since it may destroy important information for reliable deblurring such as strong edges. We propose convolutional neural network (CNN)-based down-scale methods for multi-scale deep-learning-based non-uniform single image deblurring. We argue that our CNN-based down-scaling effectively reduces the spatial dimension of the original image, while learned kernels with multiple channels may well-preserve necessary details for deblurring tasks. For each scale, we adopt to use RCAN (Residual Channel Attention Networks) as a backbone network to further improve performance. Our proposed method yielded state-of-the-art performance on GoPro dataset by large margin. Our proposed method was able to achieve 2.59dB higher PSNR than the current state-of-the-art method by Tao. Our proposed CNN-based down-scaling was the key factor for this excellent performance since the performance of our network without it was decreased by 1.98dB. The same networks trained with GoPro set were also evaluated on large-scale Su dataset and our proposed method yielded 1.15dB better PSNR than the Tao's method. Qualitative comparisons on Lai dataset also confirmed the superior performance of our proposed method over other state-of-the-art methods.

cs.CV↗

arXiv2018

Lattice-mismatched semiconductor heterostructures

Semiconductor heterostructure is a critical building block for modern semiconductor devices. However, forming semiconductor heterostructures of lattice-mismatch has been a great challenge for several decades. Epitaxial growth is infeasible to form abrupt heterostructures with large lattice-mismatch while mechanical-thermal bonding results in a high density of interface defects and therefore severely limits device applications. Here we show an ultra-thin oxide-interfaced approach for the successful formation of lattice-mismatched semiconductor heterostructures. Following the depiction of a theory on the role of interface oxide in forming the heterostructures, we describe experimental demonstrations of Ge/Si (diamond lattices), Si/GaAs (zinc blende lattice), GaAs/GaN (hexagon lattice), and Si/GaN heterostructures. Extraordinary electrical performances in terms of ideality factor, current on/off ratio, and reverse breakdown voltage are measured from p-n diodes fabricated from the four types of heterostructures, significantly outperforming diodes derived from other methods. Our demonstrations indicate the versatility of the ultra-thin-oxide-interface approach in forming lattice-mismatched heterostructures, open up a much larger possibility for material combinations for heterostructures, and pave the way toward broader applications in electronic and optoelectronic realms.

physics.app-ph↗