A property-oriented design strategy for high performance copper alloys via machine learning

High-performance copper alloys are fundamental to the lead frames of integrated circuits (ICs). For example, the traditional copper alloys, including Cu–Fe–P, Cu–Ni–Si and Cu–Cr–Zr alloys, are hardly be used in the next generation of very-large-scale integration (VLSI) ICs, which requires a ultimate tensile strength (UTS) over 800 MPa and an electrical conductivity (EC) over 50.0% To improve the mechanical and electrical properties of copper alloys, one or more alloying elements, such as Ti, Co, P, Mg, Cr, Zr, Be, and Fe, can be introduced. Many efforts have been devoted to this field and showed that the alloying elements should have little effect on the EC and possesses a large solid solubility change from high temperature to room temperature.6,7,8,9,10 However, there is a lack of a model that quantitatively describes the relationship between alloy composition and performance.