No abstract

The electronic interconnections in the state-of-the-art integrated circuit (IC) manufacturing are scaled down to the micron or sub-micron scale. This results in a dramatic increase in the current density passing through interconnections, so the electromigration (EM) effect plays a significant role in the reliability of products.Although thorough studies and reviews of EM effects have been continuously conducted in the past 60 years, some parts of EM theories lack clear elucidation of the electric current-induced non-directional effects, including the electric current-induced phase equilibrium changes. This review article is intended to provide a broad picture of Recent-proposed models (Machine learning model)Recently, Liu et al. performed the machine learning method with an

In this study, we synthesized standing 2D δ‐Cu2Te flakes (δ‐CTFs) via a facile post‐tellurization process, which served as the current collector to accommodate zinc (Zn) for AZIBs. These flakes exhibited low nucleation overpotential and low interfacial impedance, facilitating the plating/stripping of Zn ions. Interestingly, the hydrophilicity and standing structure of δ‐CTFs guided the electrodeposited Zn to laterally grow on the surface of δ‐CTFs, effectively suppressing Zn dendrite formation. The Zn@δ‐CTFs anode exhibited a long‐term cycling duration of 510 hours in a symmetric cell, which is far superior to previous reports. Even under high current density of 10 mA cm−2, the anode was able to perform stably with a cycle life of 110 hours. The machine learning model was exploited to predict the effective charge value, discovering that Zn migrated in Cu2Te were subject to the larger driving force of migration under applied field. Finally, the Zn@δ‐CTFs//MnO2 full battery exhibited excellent rate‐dependent capacity and maintained a capacity of 100 mAh g−1 after 1000 cycles at a current density of 1 A g−1, without Zn dendrite formation. This research provides a new strategy for regulating Zn deposition to address dendrite issues toward long lifespan AZIBs.

Invited for this month's cover is the group of Nano Horizons Lab hosted by Prof. Yu‐Ze Chen. The cover picture shows the lateral deposition of Zn guided by zincophilic Cu2Te flakes, resulting in the suppression of dendrite growth. Read the full text of the Research Article at 10.1002/batt.202300107.

Optimization of flank wear width (VB) progression during face milling of Inconel 718 is challenging due to the synergistic effect of cutting parameters on the complex wear mechanisms and failure modes. The lack of quantitative understanding between VB and the cutting conditions limits the development of the tool life extension. In this study, a Gaussian kernel ridge regression was employed to develop the VB progression model for face milling of Inconel 718 using multi-layer physical vapor deposition-TiAlN/NbN coated carbide inserts with the input feature of cutting speed, feed rate, axial depth of cut, and cutting length. The model showed a root-mean-square error of 30.9 (49.7) µm and R2 of 0.93 (0.81) in full fit (5-fold cross-validation test). The statistics along with the cross-plot analyses suggested that the model had a high predictive ability. A new promising condition at the cutting speed of 40 m/min, feed rate of 0.08 mm/tooth, and axial depth of cut of 0.9 mm was designed and experimentally validated. The measured and predicted VB agreed well with each other. This model is thus applicable for VB prediction and optimization in the real face milling operation of Inconel 718.