The main issue of Cu metallization is the electromigration of Cu through the interface between Cu and the barrier or capping layer. To improve electromigration resistance at the Cu and barrier metal interface, insertion of a glue layer which enhances the adhesion of Cu onto the under layer may be effective. The wettability of Cu on Ru and Ta glue layers was evaluated as the index of Cu adhesion strength onto glue layers. The wetting angle of Cu (43°) on a Ru substrate was three times lower than that of Cu (123°) on a Ta substrate after annealing. Lower wetting angle of Cu on a Ru substrate indicates a good adhesion property between Cu and Ru and may imply a high electromigration resistance. The better Cu wettability of Ru compared to Ta can be explained by the concept of lattice misfit. A Ru(002) plane has lower lattice misfit, which suggests lower interface energy, and enhanced the adhesion of Cu onto Ru. However, the Ru film showed poor Cu diffusion barrier properties, which suggests Ru should be used as a glue layer in combination with another barrier layer.

Inclusion assisted microstructure control has been a key technology to improve the toughness of C-Mn and low alloy steel welds over the last two to three decades. The microstructure of weld metals and heat affected zones (HAZs) is known to be refined by different inclusions, which may act as nucleation sites for intragranular acicular ferrite and/or to pin austenite grains thereby preventing grain growth. In the present paper, the nature of acicular ferrite and the kinetics of intragranular ferrite transformations in both weld metals and the HAZ of steels are rationalised along with nucleation mechanisms. Acicular ferrite development is considered in terms of competitive nucleation and growth reactions at austenite grain boundary and intragranular inclusion nucleation sites. It is shown that compared to weld metals, it is difficult to shift the balance of ferrite nucleation from the austenite grain boundaries to the intragranular regions in the HAZ of particle dispersed steels because inclusion densities are lower and the surface area available for ferrite nucleation at the austenite grain boundaries tends to be greater than that of intragranular inclusions. The most consistent explanation of high nucleation potency in weld metals is provided by lattice matching between ferrite and the inclusion surface to reduce the interfacial energy opposing nucleation. In contrast, an increase in the thermodynamic driving force for nucleation through manganese depletion of the austenite matrix local to the inclusion tends to be the dominant nucleation mechanism in HAZs. It is demonstrated that these means of nucleation are not mutually exclusive but depend on the nature of the nucleating phase and the prevailing transformation conditions. Issues for further improvement of weldment toughness are discussed. It is argued that greater numbers of fine particles of a type that preferentially nucleate acicular ferrite are required in particle dispersed steels to oppose the austenite grain boundary ferrite transformation and promote high volume fractions of acicular ferrite and thereby toughness.

An algorithm optimizing train running profile with Bellman's Dynamic Programming (DP) is investigated in this paper. Optimal running trajectory of a train which minimizes amount of total energy consumption has been produced under fixed origin and destination, stipulated running time and various track profile. Many previous works on this area adopt the numerical techniques of calculus of variations, Pontryagin's maximum principle, and so on. But these methods often meet some difficulties accounting for complicated actual train running preconditions, e.g. complicated functions which describe electrical motive/brake torque, local constraints of the state variable as speed limitations, nonlinear running resistance and variable grade profiles. Basic numerical DP algorithm can cope with such comlicated conditions and give the globally optimal solution. But this method consumes too large computation time for practical uses. We have made the improvements for shorter calculation time of whole optimization process and reducing the numerical error. The confined state space and irregular lattice play most important role for them. Dynamic meshing and effective utilization of system memory also realize shorter computation time. The effectiveness of the proposed method is demonstrated using various complicated running conditions.

No abstract

No abstract