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Engineering change orders (ECOs) in late stages make minimal design fixes to recover from timing shifts due to excessive IR drops. This paper integrates IR-drop-aware timing analysis and ECO timing optimization using reinforcement learning (RL). The method operates after physical design and power grid synthesis, and rectifies IR-drop-induced timing degradation through gate sizing. It incorporates the Lagrangian relaxation (LR) technique into a novel RL framework, which trains a relational graph convolutional network (R-GCN) agent to sequentially size gates to fix timing violations. The R-GCN agent outperforms a classical LR-only algorithm: in an open 45nm technology, it (a) moves the Pareto front of the delay-area tradeoff curve to the left and (b) saves runtime over the classical method by running fast inference using trained models at iso-quality. The RL model is transferable across timing specifications, and transferable to unseen designs with zero-shot learning or fine tuning.

Due to the unavailability of routing information in design stages prior to detailed routing (DR), the tasks of timing prediction and optimization pose major challenges. Inaccurate timing prediction wastes design effort, hurts circuit performance, and may lead to design failure. This work focuses on timing prediction after clock tree synthesis and placement legalization, which is the earliest opportunity to time and optimize a "complete" netlist. The paper first documents that having "oracle knowledge" of the final post-DR parasitics enables post-global routing (GR) optimization to produce improved final timing outcomes. To bridge the gap between GR-based parasitic and timing estimation and post-DR results during post-GR optimization, machine learning (ML)-based models are proposed, including the use of features for macro blockages for accurate predictions for designs with macros. Based on a set of experimental evaluations, it is demonstrated that these models show higher accuracy than GR-based timing estimation. When used during post-GR optimization, the ML-based models show demonstrable improvements in post-DR circuit performance. The methodology is applied to two different tool flows - OpenROAD and a commercial tool flow - and results on 45nm bulk and 12nm FinFET enablements show improvements in post-DR slack metrics without increasing congestion. The models are demonstrated to be generalizable to designs generated under different clock period constraints and are robust to training data with small levels of noise.

The implementation of terahertz (THz) wafer-level metadevices is critical to advance the science for applications including (I) integrated focal plane array which can image for biology and (II) integrated narrowband absorbers for high spectral resolution THz spectroscopy. Substantial progress has been made in the development of THz metamaterials; however, a wafer-level low-stress THz metadevices platform remains a challenge. This paper experimentally demonstrates a substrate-free THz metadevices platform adopting engineered Si-rich and low-stress silicon nitride (SiNx) thin films, achieving an extensive THz transparency up to f = 2.5 THz. A new analytical model is first reported from the Lorentz model that can accurately predict spectral responses of metal insulator metal (MIM) metamaterial absorbers. The model is experimentally validated in the THz range and exploited for the first demonstration of a THz absorber, which exhibits performance approaching the predicted results. Our results show that the wafer-level SiNx platform will accelerate the development of large-scale, sophisticated substrate-free THz metadevices. The Lorentz model and its quadratic model will be a very practical method for designing THz metadevices.

Microelectromechanical system (MEMS) focal plane array (FPA) with optical readout offers exciting opportunities for real-time terahertz (THz) imaging. However, conventional FPA suffers from a low THz absorption ratio, which further decreases the performance of THz imaging. Here, we present a simple and scalable approach for the realization of THz focal plane metamaterial array with a relatively high absorption ratio. The key idea is to combine the advantages of substrate-free structures with metamaterial. A 100 x100 THz FPA with a 150 x 150 {\mu}m pixel is designed, fabricated, and characterized. The dependence of the THz absorption ratio on the thickness of SiNx dielectric substrate film is investigated. The fabricated FPA exhibits a 90.6% resonant absorption at 1.36 THz, agreeing considerably with the theoretical simulation results. Our results imply that such a substrate-free THz focal plane metamaterial array enables the realization of THz imaging., Comment: 8 pages

Magnetic skyrmions are promising candidates for future information technology. Here, we present a micromagnetic study of isolated skyrmions and skyrmion clusters in ferromagnetic nanodisks driven by the spin-polarized current with spatially varied polarization. The current-driven skyrmion clusters can be either dynamic steady or static, depending on the spatially varied polarization profile. For the dynamic steady state, the skyrmion cluster moves in a circle in the nanodisk, while for the static state, the skyrmion cluster is static. The frequency of the circular motion of skyrmion is also studied. Furthermore, the dependence of the skyrmion cluster dynamics on the magnetic anisotropy and Dzyaloshinskii-Moriya interaction is investigated. Our results may provide a pathway to realize magnetic skyrmion cluster based devices., Comment: 5 pages, 5 figures