Matthew Guthaus

Paolo Pedroso, Lee-Way Wang, Matthew Guthaus

Two-phase clocking offers significant advantages in timing margin and clock flexibility, yet its adoption remains limited due to the absence of automation in modern design flows. Managing strict non-overlap and 180$^\circ$ phase separation introduces complexity in RTL implementation and timing closure, leaving two-phase clocking rare in practice. This paper presents the first fully automated two-phase clocking flow integrated into OpenROAD Flow Scripts (ORFS). Our methodology automatically transforms flip-flop-based RTL into two-phase latch-based designs using Yosys technology mapping, ABC retiming, dual clock tree synthesis, two-phase correctness validation, and full physical design from RTL-to-GDS. We implement clock-gated and recirculation mux variants, where clock-gated achieves an average 29.2\% power reduction and 50\% latch count reduction over recirculation mux. Both variants are compared against flip-flop baselines, demonstrating timing closure through time borrowing on a design that failed timing with flip-flops.

Muhammad Hadir Khan, Matthew Guthaus

Clock meshes are essential in high-performance VLSI systems for minimizing skew and handling PVT variations, but analyzing them is difficult due to reconvergent paths, multi-source driving, and input mesh buffer skew. SPICE simulations are accurate but slow; yet simplified models miss key effects like slew and input skew. We propose GATMesh, a Graph Neural Network (GNN)-based framework that models the clock mesh as a graph with augmented structural and physical features. Trained on SPICE data, GATMesh achieves high accuracy with average delay error of 5.27ps on unseen benchmarks, while achieving speed-ups of 47146x over multi-threaded SPICE simulation.