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At least 631 records · Page 35

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DOE OSTI2021

Deep Learning for Rapid Analysis of Spectroscopic Ellipsometry Data

High‐throughput experimental approaches to rapidly develop new materials require high‐throughput data analysis methods to match. Spectroscopic ellipsometry is a powerful method of optical properties characterization, but for unknown materials and/or layer structures the data analysis using traditional methods of nonlinear regression is too slow for autonomous, closed‐loop, high‐throughput experimentation. Herein, three methods (termed spectral, piecewise, and pointwise) of spectroscopic ellipsometry data analysis based on deep learning are introduced and studied. After initial training, the incremental time for inferring optical properties can be a thousand times faster than traditional methods. Results for multilayer sample structures with optically isotropic materials are presented, appropriate for high‐throughput studies of thin films of phase‐change materials such as GeSbTe (GST) alloys. Results for studies on highly birefringent layered materials are also presented, exemplified by the transition metal dichalcogenide MoS 2 . How the materials under test and the experimental objectives may guide the choice of analysis methods are discussed. The utility of our approach is demonstrated by analyzing data measured on a composition spread of GeSbTe phase‐change alloys containing 177 distinct compositions, and identifying the composition with optimal phase‐change figure of merit in only 1.4 s of analysis time.

Li, Yifei↗

DOE OSTI2024

Size Effect of Negative Capacitance State and Subthreshold Swing in Van der Waals Ferrielectric Field–Effect Transistors

Analytical calculations corroborated by the finite element modeling show that thin films of Van der Waals ferrielectrics covered by a 2D-semiconductor are promising candidates for the controllable reduction of the dielectric layer capacitance due to the negative capacitance (NC) effect emerging in the thin films. The NC state is conditioned by energy-degenerated poly-domain states of the ferrielectric polarization induced in the films under incomplete screening conditions in the presence of a dielectric layer. Calculations performed for the FET-type heterostructure “ferrielectric CuInP 2 S 6 film—2D-MoS 2 single-layer—SiO 2 dielectric layer” reveal the pronounced size effect of the multilayer capacitance. Derived analytical expressions for the electric polarization and multilayer capacitance allow to predict the thickness range of the dielectric layer and ferrielectric film for which the NC effect is the most pronounced in various Van der Waals ferrielectrics, and the corresponding subthreshold swing becomes much less than the Boltzmann's limit. Obtained results can be useful for the size and temperature control of the NC effect in the steep-slope ferrielectric FETs.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Direct‐Write Printed Contacts to Layered and 2D Materials

Advancements in fabrication methods have shaped new computing device technologies. Among these methods, depositing electrical contacts to the channel material is fundamental to device characterization. Novel layered and 2D materials are promising for next-generation computing electronic channel materials. Direct-write printing of conductive inks is introduced as a surprisingly effective, significantly faster, and cleaner method to contact different classes of layered materials, including graphene (semi-metal), MoS 2 (semiconductor), Bi-2212 (superconductor), and Fe 5 GeTe 2 (metallic ferromagnet). Based on the electrical response, the quality of the printed contacts is comparable to what is achievable with resist-based lithography techniques. These devices are tested by sweeping gate voltage, temperature, and magnetic field to show that the materials remain pristine post-processing. This work demonstrates that direct-write printing is an agile method for prototyping and characterizing the electrical properties of novel layered materials.

2D materials↗

DOE OSTI2025

Materials Selection Principles for Designing Electro‐Thermal Neurons

Artificial neurons exhibiting volatile threshold switching and action potential‐like oscillations are crucial for brain‐inspired computing. While Complimentary Metal‐Oxide‐Semiconductor (CMOS)‐based strategies require hundreds of transistors to simulate each neuron, neuronal oscillations arise spontaneously in individual electro‐thermal devices due to nonlinearities like the Mott transition in VO 2 . Despite improved understanding of the physics, quantitative connections between neuronal performance and material properties remain under‐explored, preventing predictive neuron design and rational materials selection. In this work, a physics‐aware forward design methodology is developed for interrogating a wide palette of materials with properties varying by orders of magnitude, and their performance (high frequency, high dynamical reconfigurability and low power) under external circuit and device geometry constraints is assessed. The space of viable materials is identified to be much larger than previously recognized, with candidates from a range of materials classes, including Ge, GaP and MoS 2 . CMOS‐compatible performance (such as 100 GHz oscillating frequencies) can be achieved with CMOS‐compatible node sizes (≈10 nm). Finally, combinations of material properties yielding desired neuronal performance under uncertain design constraints are considered. This work solidifies forward design principles for electro‐thermal neuron devices, a necessary pre‐condition for inverse design from desired neuronal performance to required materials properties.

compact model↗

DOE OSTI2020

Fast-Charging and Ultrahigh-Capacity Lithium Metal Anode Enabled by Surface Alloying

Li metal anodes are going through a great revival but they still encounter grand challenges. One often neglected issue is that most reported Li metal anodes are only cyclable under relatively low current density (<5 mA cm -2 ) and small areal capacity (<5 mAh cm -2 ), which essentially limits their high-power applications and results in ineffective Li utilization (<1%). Herein, it is reported that surface alloyed Li metal anodes can enable reversible cycling with ultrafast rate and ultralarge areal capacity. Low-cost Si wafers are used and are chemically etched down to 20-30 mu m membranes. Simply laminating a Si membrane onto Li foil results in the formation of LixSi alloy film fused onto Li metal with mechanical robustness and high Li-ion conductivity. Symmetric cell measurements show that the surface alloyed Li anode has excellent cycling stability, even under high current density up to 25 mA cm -2 and unprecedented areal capacity up to 100 mAh cm -2 . Furthermore, the surface alloyed Li anode is paired with amorphous MoS 3 cathode and achieves remarkable full-cell performance.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Zeolite-Stabilized Di- and Tetranuclear Molybdenum Sulfide Clusters Form Stable Catalytic Hydrogenation Sites

Supercages of faujasite (FAU)-type zeolites serve as a robust scaffold for stabilizing dinuclear (Mo 2 S 4 ) and tetranuclear (Mo 4 S 4 ) molybdenum sulfide clusters. The FAU-encaged Mo 4 S 4 clusters have a distorted cubane structure similar to the FeMo-cofactor in nitrogenase. Both clusters possess one unpaired electron per Mo atom. Additionally, they show identical catalytic activity per sulfide cluster. Their catalytic activity is stable (> 150 h) for ethene hydrogenation, while layered MoS 2 structures deactivate significantly under the same reaction conditions.