Tailoring potentials by simulation-aided design of gate layouts for spin qubit applications

• URL: http://arxiv.org/abs/2303.13358v1
• Date: Thu, 23 Mar 2023 15:36:32 GMT
• Title: Tailoring potentials by simulation-aided design of gate layouts for spin qubit applications
• Authors: Inga Seidler, Malte Neul, Eugen Kammerloher, Matthias K\"unne, Andreas Schmidbauer, Laura Diebel, Arne Ludwig, Julian Ritzmann, Andreas D. Wieck, Dominique Bougeard, Hendrik Bluhm and Lars R. Schreiber
• Abstract summary: Gate-s of spin qubit devices are commonly adapted from previous successful devices. We present a general approach for electrostatically modelling new spin qubit device layouts.
• Score: 0.4276883312743397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gate-layouts of spin qubit devices are commonly adapted from previous successful devices. As qubit numbers and the device complexity increase, modelling new device layouts and optimizing for yield and performance becomes necessary. Simulation tools from advanced semiconductor industry need to be adapted for smaller structure sizes and electron numbers. Here, we present a general approach for electrostatically modelling new spin qubit device layouts, considering gate voltages, heterostructures, reservoirs and an applied source-drain bias. Exemplified by a specific potential, we study the influence of each parameter. We verify our model by indirectly probing the potential landscape of two design implementations through transport measurements. We use the simulations to identify critical design areas and optimize for robustness with regard to influence and resolution limits of the fabrication process.

Related papers

• SMPLer: Taming Transformers for Monocular 3D Human Shape and Pose Estimation [74.07836010698801]
  We propose an SMPL-based Transformer framework (SMPLer) to address this issue. SMPLer incorporates two key ingredients: a decoupled attention operation and an SMPL-based target representation. Extensive experiments demonstrate the effectiveness of SMPLer against existing 3D human shape and pose estimation methods.
  arXiv  Detail & Related papers  (2024-04-23T17:59:59Z)
• Ab initio modelling of quantum dot qubits: Coupling, gate dynamics and robustness versus charge noise [0.0]
  Commercial semiconductor foundries can create quantum processors (QPs) using the same processes employed for conventional chips. To identify the most promising options for fabrication, one requires predictive modeling of interacting electrons in real geometries. In this work we explore a modelling method based on real-space grids, an ab initio approach without assumptions relating to device topology.
  arXiv  Detail & Related papers  (2024-02-29T23:43:22Z)
• Compositional Generative Inverse Design [69.22782875567547]
  Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem. We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples. In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
  arXiv  Detail & Related papers  (2024-01-24T01:33:39Z)
• SQuADDS: A validated design database and simulation workflow for superconducting qubit design [2.394350905741035]
  We present an open-source database of superconducting device designs that may be used as starting point for customized quantum solvers. We present a robust for achieving high accuracy on design simulations. Our database includes a front-end interface that allows users to generate bestguess'' designs based on desired circuit parameters.
  arXiv  Detail & Related papers  (2023-12-20T23:31:53Z)
• Improving Fabrication Fidelity of Integrated Nanophotonic Devices Using Deep Learning [0.4129225533930965]
  We introduce a general deep machine learning model that automatically corrects photonic device design layouts prior to first fabrication. Our model opens the door to new levels of reliability and performance in next-generation photonic circuits.
  arXiv  Detail & Related papers  (2023-03-21T18:51:17Z)
• Full Stack Optimization of Transformer Inference: a Survey [58.55475772110702]
  Transformer models achieve superior accuracy across a wide range of applications. The amount of compute and bandwidth required for inference of recent Transformer models is growing at a significant rate. There has been an increased focus on making Transformer models more efficient.
  arXiv  Detail & Related papers  (2023-02-27T18:18:13Z)
• Mega: Moving Average Equipped Gated Attention [150.3124713793503]
  Mega is a simple, theoretically grounded, single-head gated attention mechanism equipped with (exponential) moving average. We show that Mega achieves significant improvements over other sequence models, including variants of Transformers and recent state space models.
  arXiv  Detail & Related papers  (2022-09-21T20:52:17Z)
• Depth Estimation with Simplified Transformer [4.565830918989131]
  Transformer and its variants have shown state-of-the-art results in many vision tasks recently. We propose a method for self-supervised monocular Depth Estimation with simplified Transformer (DEST) Our model leads to significant reduction in model size, complexity, as well as inference latency, while achieving superior accuracy as compared to state-of-the-art.
  arXiv  Detail & Related papers  (2022-04-28T21:39:00Z)
• Inverse design of photonic devices with strict foundry fabrication constraints [55.41644538483948]
  We introduce a new method for inverse design of nanophotonic devices which guarantees that designs satisfy strict length scale constraints. We demonstrate the performance and reliability of our method by designing several common integrated photonic components.
  arXiv  Detail & Related papers  (2022-01-31T02:27:25Z)
• Efficient pre-training objectives for Transformers [84.64393460397471]
  We study several efficient pre-training objectives for Transformers-based models. We prove that eliminating the MASK token and considering the whole output during the loss are essential choices to improve performance.
  arXiv  Detail & Related papers  (2021-04-20T00:09:37Z)
• A Deep Learning Framework for Simulation and Defect Prediction Applied in Microelectronics [3.8698051494433043]
  We propose an architecture based on 3D Convolutional Neural Networks (3DCNN) in order to model the geometric variations in manufacturing parameters. We validate our framework on a microelectronics use-case using the recently published PCB scans dataset.
  arXiv  Detail & Related papers  (2020-02-25T15:54:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.