Engineering topics

Stiles, Mark D.

Publications and source records attributed to Stiles, Mark D..

5 records

DOE OSTI2023

Characterization of Noise in CMOS Ring Oscillators at Cryogenic Temperatures

Allan deviation provides a means to characterize the time-dependence of noise in oscillators and potentially identify the source characteristics. Measurements on a 130 nm, 7-stage ring oscillator show that the Allan deviation declines from 300 K to 150 K as expected, but surprisingly increases from 150 K to 11 K. At low temperatures, the measured Allan deviation can be well fit using a few random telegraph noise (RTN) sources over the range of a few kilohertz to a few gigahertz. Further, the RTN characteristics evolve to reveal an enhanced role in low-frequency noise at lower temperatures.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Probing antiferromagnetic coupling in magnetic insulator/metal heterostructures

Using depth- and element-resolved characterization, we report insights into antiferromagnetic coupling in Y 3 ⁢Fe 5 ⁢O 12 /permalloy (YIG/Py) and Y 3 ⁢Fe 5 ⁢O 12 /Co (YIG/Co) thin-film heterostructures grown on Si/SiO 2 and Gd 3⁢ Ga 5⁢ O 12 substrates. We build on recent work demonstrating antiferromagnetic coupling in polycrystalline YIG/metallic-ferromagnetic systems by characterizing differences in the structural and magnetic properties which depend on the choice of ferromagnet (Py vs Co), seed layer (with and without Pt), and substrate (Si/SiO 2 vs Gd 3 ⁢Ga 5 ⁢O 12 ). These differences in the sample structure manifest as notable changes in interface coupling sign, magnetic reversal mechanisms, magnetic depth profiles, and domain structure. Through a combination of magnetometry, polarized neutron reflectometry, and x-ray photoemission electron microscopy, a comprehensive picture of the magnetic interactions is realized, with lateral- and depth resolution at submicrometer and nanometer scales, respectively. These results confirm that both Co and Py share a preference to align antiparallel to polycrystalline YIG grown on some substrates (Si/SiO 2 and Si/SiO 2 /Pt), while coupling ferromagnetically with highly oriented YIG on (111) Gd 3⁢ Ga 5 ⁢O 12 and (110) Gd 3 ⁢Ga 5⁢ O 12 /Pt substrates. The complex interplay among magnetic interactions at the YIG/ferromagnetic interface has important implications for spintronic and magnonic devices based on this platform.

Exchange interaction↗

DOE OSTI2022

Large exotic spin torques in antiferromagnetic iron rhodium.

Spin torque is a promising tool for driving magnetization dynamics for computing technologies. These torques can be easily produced by spin-orbit effects, but for most conventional spin source materials, a high degree of crystal symmetry limits the geometry of the spin torques produced. Magnetic ordering is one way to reduce the symmetry of a material and allow exotic torques, and antiferromagnets are particularly promising because they are robust against external fields. We present spin torque ferromagnetic resonance (ST-FMR) measurements and second harmonic Hall measurements characterizing the spin torques in anti -ferromagnetic iron rhodium alloy. We report extremely large, strongly temperature-dependent exotic spin torques with a geometry apparently defined by the magnetic ordering direction. We find the spin torque efficiency of iron rhodium to be (207 +/- 94)% at 170 K and (88 +/- 32)% at room temperature. We support our conclusions with theoretical calculations showing how the antiferromagnetic ordering in iron rhodium gives rise to such exotic torques.

Gibbons, Jonathan↗

DOE OSTI2022

Quantum materials for energy-efficient neuromorphic computing: Opportunities and challenges

Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short- and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This Perspective discusses select examples of these approaches and provides an outlook on the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions

The increasing scale of neural networks and their growing application space have produced demand for more energy- and memory-efficient artificial-intelligence-specific hardware. Avenues to mitigate the main issue, the von Neumann bottleneck, include in-memory and near-memory architectures, as well as algorithmic approaches. In this report we leverage the low-power and the inherently binary operation of magnetic tunnel junctions (MTJs) to demonstrate neural network hardware inference based on passive arrays of MTJs. In general, transferring a trained network model to hardware for inference is confronted by degradation in performance due to device-to-device variations, write errors, parasitic resistance, and nonidealities in the substrate. To quantify the effect of these hardware realities, we benchmark 300 unique weight matrix solutions of a two-layer perceptron to classify the Wine dataset for both classification accuracy and write fidelity. Despite device imperfections, we achieve software-equivalent accuracy of up to 95.3% with proper tuning of network parameters in 15 x 15 MTJ arrays having a range of device sizes. The success of this tuning process shows that new metrics are needed to characterize the performance and quality of networks reproduced in mixed signal hardware.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗