Detecting defects that reduce breakdown voltage using machine learning and optical profilometry.

Semiconductor wafer manufacturing relies on the precise control of various performance metrics to ensure the quality and reliability of integrated circuits. In particular, GaN has properties that are advantageous for high voltage and high frequency power devices; however, defects in the substrate growth and manufacturing are preventing vertical devices from performing optimally. This paper explores the application of machine learning techniques utilizing data obtained from optical profilometry as input variables to predict the probability of a wafer meeting performance metrics, specifically the breakdown voltage (V).

Nanoscale Infrared Spectroscopic Characterization of Extended Defects in 4H-Silicon Carbide.

Extended defects in wide-bandgap semiconductors have been widely investigated using techniques providing either spectroscopic or microscopic information. Nano-Fourier transform infrared spectroscopy (nano-FTIR) is a nondestructive characterization method combining FTIR with nanoscale spatial resolution (∼20 nm) and topographic information. Here, we demonstrate the capability of nano-FTIR for the characterization of extended defects in semiconductors by investigating an in-grown stacking fault (IGSF) present in a 4H-SiC epitaxial layer.

Using machine learning with optical profilometry for GaN wafer screening.

To improve the manufacturing process of GaN wafers, inexpensive wafer screening techniques are required to both provide feedback to the manufacturing process and prevent fabrication on low quality or defective wafers, thus reducing costs resulting from wasted processing effort. Many of the wafer scale characterization techniques-including optical profilometry-produce difficult to interpret results, while models using classical programming techniques require laborious translation of the human-generated data interpretation methodology. Alternatively, machine learning techniques are effective at producing such models if sufficient data is available.

Optimizing performance and yield of vertical GaN diodes using wafer scale optical techniques.

To improve the manufacturing of vertical GaN devices for power electronics applications, the effects of defects in GaN substrates need to be better understood. Many non-destructive techniques including photoluminescence, Raman spectroscopy and optical profilometry, can be used to detect defects in the substrate and epitaxial layers. Raman spectroscopy was used to identify points of high crystal stress and non-uniform conductivity in a substrate, while optical profilometry was used to identify bumps and pits in a substrate which could cause catastrophic device failures.

Room Temperature Skyrmions in Strain-Engineered FeGe thin films.

Skyrmions hold great promise for low-energy consumption and stable high density information storage, and stabilization of the skyrmion lattice (SkX) phase at or above room temperature is greatly desired for practical use. The topological Hall effect can be used to identify candidate systems above room temperature, a challenging regime for direct observation by Lorentz electron microscopy. Atomically ordered FeGe thin films are grown epitaxially on Ge(111) substrates with ~ 4 % tensile strain.

Experimental observation of soliton propagation and annihilation in a hydromechanical array of one-way coupled oscillators.

We have experimentally realized unidirectional or one-way coupling in a mechanical array by powering the coupling with flowing water. In cyclic arrays with an even number of elements, solitonlike waves spontaneously form but eventually annihilate in pairs, leaving a spatially alternating static attractor. In cyclic arrays with an odd number of elements, this alternating attractor is topologically impossible, and a single soliton always remains to propagate indefinitely.