Genetic Design of Enhanced Valley Splitting towards a Spin Qubit in Silicon
Lijun Zhang, Jun-Wei Luo, A. L. Saraiva, Belita Koiller, Alex Zunger

TL;DR
This paper demonstrates that ordered Ge-Si layered barriers can significantly enhance valley splitting in silicon quantum wells, enabling more reliable spin qubits for quantum computing through a genetic algorithm optimized design.
Contribution
It introduces a novel atomistic design of superlattice barriers using genetic algorithms to maximize valley splitting in silicon quantum wells.
Findings
Valley splitting can be increased up to ~9 meV with optimized layer sequences.
Ordered Ge-Si superlattices outperform random alloy barriers in valley splitting.
Enhanced valley splitting is robust against inter-layer mixing.
Abstract
Electronic spins in Silicon (Si) are rising contenders for qubits -- the logical unit of quantum computation-- owing to its outstanding spin coherence properties and compatibility to standard electronics. A remarkable limitation for spin quantum computing in Si hosts is the orbital degeneracy of this material's conduction band, preventing the spin-1/2 states from being an isolated two-level system. So far available samples of Si quantum wells cladded by Ge-Si alloy barriers provide relatively small valley splitting (VS), with the order of 1 meV or less, degrading the fidelity of qubits encoded in spin "up" and "down" states in Si. Here, based on an atomically resolved pseudopotential theory, we demonstrate that ordered Ge-Si layered barriers confining a Si slab can be harnessed to enhance the VS in the active Si region by up to one order of magnitude compared to the random alloy…
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Taxonomy
TopicsAdvancements in Semiconductor Devices and Circuit Design · Semiconductor Quantum Structures and Devices · Photonic and Optical Devices
