RESUMO
The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions and each device realisation requires a different tuning protocol. We demonstrate that it is possible to automate the tuning of a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate Ge/SiGe heterostructure double quantum dot device from scratch with the same algorithm. We achieve tuning times of 30, 10, and 92 min, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices, allowing for the characterization of the regions where double quantum dot regimes are found. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning.
RESUMO
Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies.
RESUMO
We report experimental evidence of ballistic hole transport in one-dimensional quantum wires gate-defined in a strained SiGe/Ge/SiGe quantum well. At zero magnetic field, we observe conductance plateaus at integer multiples of 2 e2/ h. At finite magnetic field, the splitting of these plateaus by Zeeman effect reveals largely anisotropic g-factors with absolute values below 1 in the quantum-well plane, and exceeding 10 out-of-plane. This g-factor anisotropy is consistent with a heavy-hole character of the propagating valence-band states, which is in line with a predominant confinement in the growth direction. Remarkably, we observe quantized ballistic conductance in device channels up to 600 nm long. These findings mark an important step toward the realization of novel devices for applications in quantum spintronics.
RESUMO
Neurofibromatosis type 1 (NF1) is one of the most common cancer predisposing syndromes with an incidence of 1 in 3,500 worldwide. Certain neoplasms or malignancies are over-represented in individuals with NF1; however, an increased risk of breast cancer has not been widely recognized or accepted. We identified 76 women with NF1 seen in the Henry Ford Health System (HFHS) from 1990 to 2009, and linked them to the Surveillance Epidemiology and End Results (SEER) registry covering the metropolitan Detroit area. Fifty-one women (67%) were under age 50 years at the time of data analysis. Six women developed invasive breast cancer before age 50, and three developed invasive breast cancer after age 50. Using standardized incidence ratios (SIRs) calculated based on the SEER age-adjusted invasive breast cancer incidence rates, our findings demonstrated a statistically significant increase of breast cancer incidence occurring in NF1 women (SIR = 5.2; 95% CI 2.4-9.8), and this relative increase was especially evident among those with breast cancer onset under age 50 (SIR = 8.8; 95% CI 3.2-19.2). These data are consistent with other reports suggesting an increase in breast cancer risk among females with NF1, which indicate that breast cancer screening guidelines should be evaluated for this potentially high-risk group.