Influence of self-heating on the millimeter-wave and terahertz performance of MBE grown silicon IMPATT diodes

The influence of self-heating on the millimeter-wave(mm-wave)and terahertz(THz)performance of double-drift region(DDR)impact avalanche transit time(IMPATT)sources based on silicon(Si)has been investigated in this paper.The dependences of static and large-signal parameters on junction temperature are estimated using a non-sinusoidal voltage excited(NSVE)large-signal simulation technique developed by the authors,which is based on the quantum-corrected drift-diffusion(QCDD)model.Linear variations of static parameters and non-linear variations of large-signal parameters with temperature have been observed.Analytical expressions representing the temperature dependences of static and large-signal parameters of the diodes are developed using linear and 2nd degree polynomial curve fitting techniques,which will be highly useful for optimizing the thermal design of the oscillators.Finally,the simulated results are found to be in close agreement with the experimentally measured data.

Transfer learning prediction on lithium-ion battery heat release under thermal runaway condition

Accurately predicting the variability of thermal runaway(TR)behavior in lithium-ion(Li-ion)batteries is critical for designing safe and reliable energy storage systems.Unfortunately,traditional calorimetry-based experiments to measure heat release during TR are time-consuming and expensive.Herein,we highlight an exciting transfer learning approach that leverages mass ejection data and metadata from cells to predict heat output variability during TR events.This approach significantly reduces the effort and time to assess thermal risks associated with Li-ion batteries.