M. G. Lagally's research while affiliated with University of Wisconsin–Madison and other places

This work demonstrates that the electrodeposition of highly stressed films on compliant ribbons is a robust process to obtain helical structures with excellent mechanical stability and potentially high thermal and electrical conductance. Electrodeposition on end‐tethered ribbons alters their axial and bending stiffness while imparting mechanical stress to drive the formation of a helix with a microscale diameter and pitch in a controlled and scalable manner. The process generates helices with diameters and pitches between 80 and 200 µm and lengths as large as several millimeters. The approach is amenable to parallel processing a large number of 3D structures on any substrate, including large‐area semiconductor wafers. This phenomenon is explained in terms of the change of stress gradients as material is added. Applications of the fabricatd helices include antennas, metamaterials, and slow‐wave structures in frequency ranges not previously attainable.

We report gold electroplating on ultra-compliant substrates comprising helical slow wave structures (SWSs) for traveling wave tube amplifiers (TWTAs) [1]. The novel ultra-compliant substrates are composed of edge-tethered tri-layer metal ribbons with a helical geometry of microscale diameter. After electroplating with gold, we obtain overall thicknesses of a few um. We discuss different controllable electroplating conditions that influence thickness, uniformity, roughness, and related properties of deposited gold films on helical ribbons. In addition to increasing conductance of the electroplated helical ribbons, electroplating stabilizes the helix at its equilibrium diameter, with the pitch predicted by Prakash et al .[1]. Our method of fabrication of ultra-compliant helical ribbons starts with defining strips of width 5 µm to 10 µm by optical lithography, metal evaporation, and lift-off, deposited on Si substrates coated with a sacrificial layer of Ge or GeOx,. We use Cr/Au/Cr tri-layers to create an inherent stress gradient that causes the strip to self-assemble into a helix, after etching with XeF 2 for selective removal of a sacrificial layer. Diameter and pitch of the released helices are controlled by varying the thickness, the elastic modulus, the residual stress, and the in-plane geometry of the deposited tri-layer metal strips. The gold electroplating process uses a sulphate-based gold solution in a two-electrode electrochemical setup with the helix as the cathode and a platinized mesh as the anode [2]. We deposit gold to a thickness of a few um, using a pulsed current source with variable parameters. Direct current can also be used with smaller deposition times. Our results demonstrate the application of electroplating to unconventional ultra-compliant helix of nanoscale dimensions. Reference [1] Divya J. Prakash,., Matthew M. Dwyer, Marcos Martinez Argudo, Mengistie L. Debasu, Hassan Dibaji, Max G. Lagally, Daniel W. van der Weide, and Francesca Cavallo. 2020. “Self-Winding Helices as Slow-Wave Structures for Sub-Millimeter Traveling-Wave Tubes.” ACS Nano, 2021, 15, 1229-1239. doi:10.1021/acsnano.0c08296. [2] Max G. Lagally, Anjali Chaudhary, Daniel van der Weide, Divya J. Prakash, and Francesca Cavallo, Improved Self-assembly of Helices via Electrodeposition on Freestanding Nanoribbons for TWT Application, provisional US patent application (P220249US01) Work supported by U.S. AFOSR-Award No FA9550-22-1-0086.

Submicrometer-thick layers of hexagonal boron nitride (hBN) exhibit high in-plane thermal conductivity and useful optical properties, and serve as dielectric encapsulation layers with low electrostatic inhomogeneity for graphene devices. Despite the promising applications of hBN as a heat spreader, the thickness dependence of its cross-plane thermal conductivity is not known, and the cross-plane phonon mean free paths (MFPs) have not been measured. We measure the cross-plane thermal conductivity of hBN flakes exfoliated from bulk crystals. We find that submicrometer thick flakes exhibit thermal conductivities up to 8.1 ± 0.5 W m-1 K-1 at 295 K, which exceeds previously reported bulk values by more than 60%. Surprisingly, the average phonon mean free path is found to be several hundred nanometers at room temperature, a factor of 5 larger than previous predictions. When planar twist interfaces are introduced into the crystal by mechanically stacking multiple thin flakes, the cross-plane thermal conductivity of the stack is found to be a factor of 7 below that of individual flakes with similar total thickness, thus providing strong evidence that phonon scattering at twist boundaries limits the maximum phonon MFPs. These results have important implications for hBN integration in nanoelectronics and improve our understanding of thermal transport in two-dimensional materials.

Large-scale arrays of quantum-dot spin qubits in Si/SiGe quantum wells require large or tunable energy splittings of the valley states associated with degenerate conduction band minima. Existing proposals to deterministically enhance the valley splitting rely on sharp interfaces or modifications in the quantum well barriers that can be difficult to grow. Here, we propose and demonstrate a new heterostructure, the “Wiggle Well”, whose key feature is Ge concentration oscillations inside the quantum well. Experimentally, we show that placing Ge in the quantum well does not significantly impact our ability to form and manipulate single-electron quantum dots. We further observe large and widely tunable valley splittings, from 54 to 239 μeV. Tight-binding calculations, and the tunability of the valley splitting, indicate that these results can mainly be attributed to random concentration fluctuations that are amplified by the presence of Ge alloy in the heterostructure, as opposed to a deterministic enhancement due to the concentration oscillations. Quantitative predictions for several other heterostructures point to the Wiggle Well as a robust method for reliably enhancing the valley splitting in future qubit devices. Quantum-dot spin qubits in Si/SiGe quantum wells require a large and uniform valley splitting for robust operation and scalability. Here the authors introduce and characterize a new heterostructure with periodic oscillations of Ge atoms in the quantum well, which could enhance the valley splitting.

We investigate the interaction between an electron beam and a THz guided electromagnetic wave in a helical slow-wave structure formed by self-assembly of a conductive ribbon. We have previously shown the controlled fabrication of this slow-wave structure and its potential to form the basis for widely deployable millimeter-through-THz traveling-wave tube amplifiers. The process allows the fabrication of helical slow-wave structures with single and double chirality. Here, we use three-dimensional simulations to perform a comparative analysis of beam–wave interaction in self-assembled gold helices with single and double chirality. First, the structures are modeled without the electron beam (cold helices) to calculate the distribution of the electric field generated by the high-frequency wave. We perform simulations of cold helices by using Computer Simulation Technology Microwave Studio. Second, we evaluate the interaction between an electron beam and the THz travelingwave by using a particle in cell simulator in Computer Simulation Technology Particle Studio. Simulation studies show that a switch in chirality in the middle of self-assembled helices generates a reflected wave that boosts beam–wave interaction. We demonstrate that this efficient energy exchange will potentially provide high gain in THz traveling-wave tube amplifiers based on self-assembled helices.

The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low-quality data is processed by an autonomous system. In this work, we propose a framework for robust autotuning of QD devices that combines a machine learning (ML) state classifier with a data quality control module. The data quality control module acts as a “gatekeeper” system, ensuring that only reliable data are processed by the state classifier. Lower data quality results in either device recalibration or termination. To train both ML systems, we enhance the QD simulation by incorporating synthetic noise typical of QD experiments. We confirm that the inclusion of synthetic noise in the training of the state classifier significantly improves the performance, resulting in an accuracy of 95.0(9)% when tested on experimental data. We then validate the functionality of the data quality control module by showing that the state classifier performance deteriorates with decreasing data quality, as expected. Our results establish a robust and flexible ML framework for autonomous tuning of noisy QD devices.