Dual-Gate All-Electrical Valleytronic Transistors.

The development of integrated circuits (ICs) based on a complementary metal-oxide-semiconductor through transistor scaling has reached the technology bottleneck; thus, alternative approaches from new physical mechanisms are highly demanded. Valleytronics in two-dimensional (2D) material systems has recently emerged as a strong candidate, which utilizes the valley degree of freedom to process information for electronic applications. However, for all-electrical valleytronic transistors, very low room-temperature "valley on-off" ratios (around 10) have been reported so far, which seriously limits their practical applications. In this work, we successfully illustrated both n- and p-type valleytronic transistor performances in monolayer MoS2 and WSe2 devices, with measured "valley on-off" ratios improved up to 3 orders of magnitude greater compared to previous reports. Our work shows a promising way for the electrically controllable manipulation of valley degree of freedom toward practical device applications.

A machine learning approach for accurate and real-time DNA sequence identification.

BACKGROUND: The all-electronic Single Molecule Break Junction (SMBJ) method is an emerging alternative to traditional polymerase chain reaction (PCR) techniques for genetic sequencing and identification. Existing work indicates that the current spectra recorded from SMBJ experimentations contain unique signatures to identify known sequences from a dataset. However, the spectra are typically extremely noisy due to the stochastic and complex interactions between the substrate, sample, environment, and the measuring system, necessitating hundreds or thousands of experimentations to obtain reliable and accurate results. RESULTS: This article presents a DNA sequence identification system based on the current spectra of ten short strand sequences, including a pair that differs by a single mismatch. By employing a gradient boosted tree classifier model trained on conductance histograms, we demonstrate that extremely high accuracy, ranging from approximately 96 % for molecules differing by a single mismatch to 99.5 % otherwise, is possible. Further, such accuracy metrics are achievable in near real-time with just twenty or thirty SMBJ measurements instead of hundreds or thousands. We also demonstrate that a tandem classifier architecture, where the first stage is a multiclass classifier and the second stage is a binary classifier, can be employed to boost the single mismatched pair's identification accuracy to 99.5 %. CONCLUSIONS: A monolithic classifier, or more generally, a multistage classifier with model specific parameters that depend on experimental current spectra can be used to successfully identify DNA strands.

All-electrical switching of a topological non-collinear antiferromagnet at room temperature.

Non-collinear antiferromagnetic Weyl semimetals, combining the advantages of a zero stray field and ultrafast spin dynamics, as well as a large anomalous Hall effect and the chiral anomaly of Weyl fermions, have attracted extensive interest. However, the all-electrical control of such systems at room temperature, a crucial step toward practical application, has not been reported. Here, using a small writing current density of around 5 × 106 A·cm-2, we realize the all-electrical current-induced deterministic switching of the non-collinear antiferromagnet Mn3Sn, with a strong readout signal at room temperature in the Si/SiO2/Mn3Sn/AlOx structure, and without external magnetic field or injected spin current. Our simulations reveal that the switching originates from the current-induced intrinsic non-collinear spin-orbit torques in Mn3Sn itself. Our findings pave the way for the development of topological antiferromagnetic spintronics.