Designing nanoscale electronic devices such as the currently manufactured nanoribbon field-effect transistors (NRFETs) requires advanced modeling tools capturing all relevant quantum mechanical effects. State-of-the-art approaches combine the non-equilibrium Green’s function (NEGF) formalism and density functional theory (DFT). However, as device dimensions do not exceed a few nanometers anymore, electrons are confined in ultra-small volumes, giving rise to strong electron-electron interactions. To account for these critical effects, DFT+NEGF solvers should be extended with the GW approximation, which massively increases their computational intensity. Here, we present the first implementation of the NEGF+GW scheme capable of handling NRFET geometries with dimensions comparable to experiments. This package, called QuaTrEx, makes use of a novel spatial domain decomposition scheme, can treat devices made of up to 84,480 atoms, scales very well on the Alps and Frontier supercomputers (> 80% weak scaling efficiency), and sustains an exascale FP64 performance on 42,240 atoms (1.15 Eflop/s).

Experimental development of gate-all-around silicon nanowire field-effect transistors (NWFETs), a viable replacement for FinFETs, can be complemented by technology computer-aided design. This requires the availability of advanced device simulators relying on a quantum transport (QT) approach without any empirical parameters as inputs. Concretely, all material properties should be described from first-principles, and the whole physics at play should be accurately modeled, particularly the strong electron-electron interactions occurring in highly confined structures such as NWFETs. To shed light on these many-body effects, we implement them within the self-consistent GW approximation into an ab initio QT solver called QuaTrEx, based on density functional theory and the Non-equilibrium Green’s Function formalism. We then simulate transistors made of up to 10,560 atoms on the LUMI supercomputer’s GPU partition, reaching a parallel efficiency of $\mathbf{7 4 \%}(\mathbf{6 0 \%}$) in weak (strong) scaling and an overall computational performance of 69.3 Pflop/s in double precision on 1,800 nodes.