arXiv subjects

Kye Okabe

Publications and source records attributed to Kye Okabe.

3 recordsLinked to original sources

arXiv2020

Electrical Tuning of Phase Change Antennas and Metasurfaces

The success of semiconductor electronics is built on the creation of compact, low-power switching elements that offer routing, logic, and memory functions. The availability of nanoscale optical switches could have a similarly transformative impact on the development of dynamic and programmable metasurfaces, optical neural networks, and quantum information processing. Phase change materials are uniquely suited to enable their creation as they offer high-speed electrical switching between amorphous and crystalline states with notably different optical properties. Their high refractive index has also been harnessed to fashion them into compact optical antennas. Here, we take the next important step by realizing electrically-switchable phase change antennas and metasurfaces that offer strong, reversible, non-volatile, multi-phase switching and spectral tuning of light scattering in the visible and near-infrared spectral ranges. Their successful implementation relies on a careful joint thermal and optical optimization of the antenna elements that comprise an Ag strip that simultaneously serves as a plasmonic resonator and a miniature heating stage.

physics.optics↗

arXiv2017

Training a Fully Convolutional Neural Network to Route Integrated Circuits

We present a deep, fully convolutional neural network that learns to route a circuit layout net with appropriate choice of metal tracks and wire class combinations. Inputs to the network are the encoded layouts containing spatial location of pins to be routed. After 15 fully convolutional stages followed by a score comparator, the network outputs 8 layout layers (corresponding to 4 route layers, 3 via layers and an identity-mapped pin layer) which are then decoded to obtain the routed layouts. We formulate this as a binary segmentation problem on a per-pixel per-layer basis, where the network is trained to correctly classify pixels in each layout layer to be 'on' or 'off'. To demonstrate learnability of layout design rules, we train the network on a dataset of 50,000 train and 10,000 validation samples that we generate based on certain pre-defined layout constraints. Precision, recall and $F_1$ score metrics are used to track the training progress. Our network achieves $F_1\approx97\%$ on the train set and $F_1\approx92\%$ on the validation set. We use PyTorch for implementing our model. Code is made publicly available at https://github.com/sjain-stanford/deep-route .

cs.CV↗

arXiv2016

High Current Density and Low Thermal Conductivity of Atomically Thin Semimetallic WTe2

Two-dimensional (2D) semimetals beyond graphene have been relatively unexplored in the atomically-thin limit. Here we introduce a facile growth mechanism for semimetallic WTe2 crystals, then fabricate few-layer test structures while carefully avoiding degradation from exposure to air. Low-field electrical measurements of 80 nm to 2 um long devices allow us to separate intrinsic and contact resistance, revealing metallic response in the thinnest encapsulated and stable WTe2 devices studied to date (3 to 20 layers thick). High-field electrical measurements and electro-thermal modeling demonstrate that ultra-thin WTe2 can carry remarkably high current density (approaching 50 MA/cm2, higher than most common interconnect metals) despite a very low thermal conductivity (of the order ~3 W/m/K). These results suggest several pathways for air-stable technological viability of this layered semimetal.

cond-mat.mtrl-sci↗