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Bing Dai

Publications and source records attributed to Bing Dai.

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High Thermal Conductivity of Back-End-of-Line Compatible Diamond Films

Back-end-of-line (BEOL) thermal management requires electrically insulating heat-spreading dielectric that can be integrated within thermal budgets below 400 C. Here, we report polycrystalline diamond films grown directly on Si at a substrate temperature below 400C. Two films with average thickness of 760 and 1000 nm were characterized by Raman spectroscopy, scanning electron microscopy (SEM), and time-domain thermoreflectance (TDTR). Raman spectra show a sharp diamond peak with minor signatures of non-diamond carbon, while SEM reveals lateral growth and large grain size. Temperature dependent TDTR measurements were performed from room temperature to 100C. Sensitivity analysis indicates that the sensitivity of cross-plane thermal conductivity is comparative to the in-plane thermal conductivity. Accordingly, the films were analyzed using an isotropic thermal model by considering the nearly-isotropic grain structure, yielding room temperature effective thermal conductivity of 73 and 86 W m-1 K-1, respectively. These values are about two orders of magnitude higher than those of conventional dielectric materials and demonstrate the potential of diamond films grown at low temperatures as dielectric heat-spreading layers.

cond-mat.mtrl-sci

Semi-Supervised Recognition under a Noisy and Fine-grained Dataset

Simi-Supervised Recognition Challenge-FGVC7 is a challenging fine-grained recognition competition. One of the difficulties of this competition is how to use unlabeled data. We adopted pseudo-tag data mining to increase the amount of training data. The other one is how to identify similar birds with a very small difference, especially those have a relatively tiny main-body in examples. We combined generic image recognition and fine-grained image recognition method to solve the problem. All generic image recognition models were training using PaddleClas . Using the combination of two different ways of deep recognition models, we finally won the third place in the competition.

cs.CV