Molecular dynamics study of thermal conductivities of cubic diamond,lonsdaleite,and nanotwinned diamond via machine-learned potential

Diamond is a wide-bandgap semiconductor with a variety of crystal configurations,and has the potential applications in the field of high-frequency,radiation-hardened,and high-power devices.There are several important polytypes of diamonds,such as cubic diamond,lonsdaleite,and nanotwinned diamond(NTD).The thermal conductivities of semiconductors in high-power devices at different temperatures should be calculated.However,there has been no reports about thermal conductivities of cubic diamond and its polytypes both efficiently and accurately based on molecular dynamics(MD).Here,using interatomic potential of neural networks can provide obvious advantages.For example,comparing with the use of density functional theory(DFT),the calculation time is reduced,while maintaining high accuracy in predicting the thermal conductivities of the above-mentioned three diamond polytypes.Based on the neuroevolution potential(NEP),the thermal conductivities of cubic diamond,lonsdaleite,and NTD at 300 K are respectively 2507.3 W·m^(-1)·K^(-1),1557.2 W·m^(-1)·K^(-1),and 985.6 W·m^(-1)·K^(-1),which are higher than the calculation results based on Tersoff-1989 potential(1508 W·m^(-1)·K^(-1),1178 W·m^(-1)·K^(-1),and 794 W·m^(-1)·K^(-1),respectively).The thermal conductivities of cubic diamond and lonsdaleite,obtained by using the NEP,are closer to the experimental data or DFT data than those from Tersoff-potential.The molecular dynamics simulations are performed by using NEP to calculate the phonon dispersions,in order to explain the possible reasons for discrepancies among the cubic diamond,lonsdaleite,and NTD.In this work,we propose a scheme to predict the thermal conductivity of cubic diamond,lonsdaleite,and NTD precisely and efficiently,and explain the differences in thermal conductivity among cubic diamond,lonsdaleite,and NTD.

Nondestructive monitoring of annealing and chemical-mechanical planarization behavior using ellipsometry and deep learning

The Cu-flling process in through-silicon via(TSV-Cu)is a key technology for chip stacking and three-dimensional vertical packaging.During this process,defects resulting from chemical-mechanical planarization(CMP)and annealing severely affect the reliability of the chips.Traditional methods of defect characterization are destructive and cumbersome.In this study,a new defect inspection method was developed using Mueller matrix spectroscopic ellipsometry.TSV-Cu with a 3-μm-diameter and 8-μm-deep Cu filling showed three typical types of characteristics:overdishing(defect-OD),protrusion(defect-P),and defect-free.The process dimension for each defect was 13 nm.First,the three typical defects caused by CMP and annealing were investigated.With single-channel deep learning and a Mueller matrix element(MME),the TSV-Cu defect types could be distinguished with an accuracy rate of 99.94%.Next,seven effective MMEs were used as independent channels in the artificial neural network to quantify the height variation in the Cu flling in the z-direction.The accuracy rate was 98.92%after training,and the recognition accuracy reached 1 nm.The proposed approach rapidly and nondestructively evaluates the annealing bonding performance of CMP processes,which can improve the reliability of high-density integration.

TSLocator: A Transformer-Based Approach to Bug Localization

For projects with thousands of files, finding the locations of bugs is time-consuming and labor-intensive. Bug reports as a potential resource to help locate bugs in source codes have been used to design automatic tools to solve this problem. Existing information retrieval(IR)-based bug localization methods rely heavily on the similarity score between bug report and historical reports. As deep learning methods show great advantages in calculating text semantic similarity, we adapt the transformer network with IR-based bug localization methods to design a novel approach, TSLocator, to bug localization. In TSLocator, we propose five new features between bug reports and source codes. We use SVMRank to model the relation between all the six features and the actual buggy file. Given a new bug report, TSLocator automatically calculates the features and linearly weights the features to produce a suspicious score for all candidate files. TSLocator recommends a list of suspicious buggy files ranked by the score. The experimental results show that TSLocator outperforms existing methods in accuracy and performance of bug localization.