Paper Reading AI Learner

Nano selective area growth of GaN by MOVPE on 4H-SiC using epitaxial graphene as a mask: towards integrated III-nitride / graphene / SiC electronics and optoelectronics

2015-10-15 12:52:04
Renaud Puybaret, Gilles Patriarche, Matthew B. Jordan, Suresh Sundaram, Youssef El Gmili, Jean-Paul Salvestrini, Paul L. Voss, Walt A. de Heer, Claire Berger, Abdallah Ougazzaden
       

Abstract

We report the growth of high-quality triangular GaN nanomesas, 30-nm thick, on the C-face of 4H-SiC using nano selective area growth (NSAG) with patterned epitaxial graphene grown on SiC as an embedded mask. NSAG alleviates the problems of defective crystals in the heteroepitaxial growth of nitrides, and the high mobility graphene film can readily provide the back low-dissipative electrode in GaN-based optoelectronic devices. The process consists in first growing a 5-8 graphene layers film on the C-face of 4H- SiC by confinement-controlled sublimation of silicon carbide. The graphene film is then patterned and arrays of 75-nanometer-wide openings are etched in graphene revealing the SiC substrate. 30-nanometer-thick GaN is subsequently grown by metal organic vapor phase epitaxy. GaN nanomesas grow epitaxially with perfect selectivity on SiC, in openings patterned through graphene, with no nucleation on graphene. The up-or-down orientation of the mesas on SiC, their triangular faceting, and cross-sectional scanning transmission electron microscopy show that they are biphasic. The core is a zinc blende monocrystal surrounded with single-crystal hexagonal wurtzite. The GaN crystalline nanomesas have no threading dislocations, and do not show any V-pit. This NSAG process potentially leads to integration of high-quality III-nitrides on the wafer scalable epitaxial graphene / silicon carbide platform.

Abstract (translated)

URL

https://arxiv.org/abs/1510.04513

PDF

https://arxiv.org/pdf/1510.04513.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot