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Memristor-based cryogenic programmable DC sources supporting measurement data
- Citation Author(s):
- Submitted by:
- Pierre-Antoine Mouny
- Last updated:
- Fri, 12/16/2022 - 09:08
- DOI:
- 10.21227/f1vm-k153
- Data Format:
- License:
- Categories:
- Keywords:
Abstract
Current quantum systems that are based on spin qubits are controlled by classical electronics located outside the cryostat at room temperature. This approach creates a major wiring bottleneck, which is one of the main roadblocks toward truly scalable quantum computers. Thus, we propose a scalable memristor-based programmable DC source that can be used to perform biasing of quantum dots inside the cryostat (i.e., in-situ). This novel cryogenic approach would enable us to control the applied voltage on the electrostatic gates by programming the resistance of the memristors, thus storing in the latter the appropriate conditions to form the quantum dots. In this study, we first demonstrate multilevel resistance programming of TiO2-based memristors at 4.2 K, which is an essential feature to achieve voltage tunability of the memristor-based DC source. We then report hardware-based simulations of the electrical performance of the proposed DC source. A cryogenic TiO2-based memristor model fitted on our experimental data at 4.2 K was used to show a 1 V voltage range and 100 μV in-situ memristor-based DC source. Finally, we simulate the biasing of double quantum dots, enabling sub-2 minutes in-situ charge stability diagrams. This demonstration is a first step towards more advanced cryogenic applications for resistive memories, such as cryogenic control electronics for quantum computers.
Experimental data repository for the article: Memristor-based cryogenic programmable DC sources for scalable in-situ quantum-dot control All simulation data are reproductible using the accessible GitHub code: https://github.com/3it-nano/QDMS.
simple_convergence files are multilevel programming data (.txt), simple_convergence_overlap_X_ohm are the read variability measurements and I_V-t Sampling (.csv) are the retention measurments.
Experimentalist: Pierre-Antoine Mouny (Université de Sherbrooke)
Dataset Files
- MeasurementData_memristor.zip (171.39 MB)
- data_revision.rar (523.04 kB)