Machine learning force field for thermal oxidation of silicon.

Looking back at seven decades of highly extensive application in the semiconductor industry, silicon and its native oxide SiO2 are still at the heart of several technological developments. Recently, the fabrication of ultra-thin oxide layers has become essential for keeping up with trends in the down-scaling of nanoelectronic devices and for the realization of novel device technologies. With this comes a need for better understanding of the atomic configuration at the Si/SiO2 interface.

Over- and Undercoordinated Atoms as a Source of Electron and Hole Traps in Amorphous Silicon Nitride (a-SiN).

Silicon nitride films are widely used as the charge storage layer of charge trap flash (CTF) devices due to their high charge trap densities. The nature of the charge trapping sites in these materials responsible for the memory effect in CTF devices is still unclear. Most prominently, the Si dangling bond or -center has been identified as an amphoteric trap center.

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies.

The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the characterization of thousands of MEPs. Given the difficulty of developing reliable and accurate algorithms, currently the assessment of MEPs is performed with visual inspection and manual annotation by a medical expert; making it a time-consuming, inaccurate, and error-prone process.

Machine learning interatomic potential for silicon-nitride (Si3N4) by active learning.

Silicon nitride (Si3N4) is an extensively used material in the automotive, aerospace, and semiconductor industries. However, its widespread use is in contrast to the scarce availability of reliable interatomic potentials that can be employed to study various aspects of this material on an atomistic scale, particularly its amorphous phase. In this work, we developed a machine learning interatomic potential, using an efficient active learning technique, combined with the Gaussian approximation potential (GAP) method.

Multi-locus transcranial magnetic stimulation system for electronically targeted brain stimulation.

Background: Transcranial magnetic stimulation (TMS) allows non-invasive stimulation of the cortex. In multi-locus TMS (mTMS), the stimulating electric field (E-field) is controlled electronically without coil movement by adjusting currents in the coils of a transducer.

Objective: To develop an mTMS system that allows adjusting the location and orientation of the E-field maximum within a cortical region.