Advanced technology nodes are beginning to adopt various technologies including innovations in transistor structure and MPT (Multi Patterning Technology) to achieve BEOL (Back End of Line) scaling. During DTCO (Design-Technology Co-Optimization) activity, BEOL geometries can be explored to achieve target PPA (Power-Performance-Area). To improve design's PPA, wire delay per unit distance can be reduced by increasing the metal width and spacing with given metal thickness. But increasing metal width and spacing can negatively affect the performance of the design as wire track resources per unit area reduced. Strengthening PDN (Power Delivery Network) can improve IR-drop of design, but also impact design's PPA negatively by consuming BEOL resource more. In this paper, application-driven metal stack and PDN optimization using ML (Machine-Learning) technique presented to address the issue effectively. To optimize metal stack and PDN, parameters of layer sheet count per thickness, pitch, spacing, and PDN horizontal/Vertical pitches are explored by Synopsys DSO.ai ML framework. DSO.ai optimization is constrained to maximize achieved frequency while maintaining certain IR-drop target. The metal stack and PDN optimization improved +2.2% of achieved frequency while 5.5% worse IR-drop. This is better frequency improvement than +1.4% of achieved frequency while 2.5% worse IR-drop from closest space where the DTCO done by human experts.
|