skip to main content

Title: 18.1 A Self-Health-Learning GaN Power Converter Using On-Die Logarithm-Based Analog SGD Supervised Learning and Online T j -Independent Precursor Measurement
; ;
Award ID(s):
Publication Date:
Journal Name:
2020 IEEE International Solid- State Circuits Conference - (ISSCC)
Page Range or eLocation-ID:
286 to 288
Sponsoring Org:
National Science Foundation
More Like this
  1. The unusual correlated state that emerges in URu 2 Si 2 below T HO = 17.5 K is known as “hidden order” because even basic characteristics of the order parameter, such as its dimensionality (whether it has one component or two), are “hidden.” We use resonant ultrasound spectroscopy to measure the symmetry-resolved elastic anomalies across T HO . We observe no anomalies in the shear elastic moduli, providing strong thermodynamic evidence for a one-component order parameter. We develop a machine learning framework that reaches this conclusion directly from the raw data, even in a crystal that is too small for traditional resonant ultrasound. Our result rules out a broad class of theories of hidden order based on two-component order parameters, and constrains the nature of the fluctuations from which unconventional superconductivity emerges at lower temperature. Our machine learning framework is a powerful new tool for classifying the ubiquitous competing orders in correlated electron systems.