Highly Reliable Ovonic Threshold Switch with TiN/GeTe/TiN Structure

A new architecture has become necessary owing to the power consumption and latency problems of the von Neumann architecture. A neuromorphic memory system is a promising candidate for the new system as it has the potential to process large amounts of digital information. A crossbar array (CA), which consists of a selector and a resistor, is the basic building block for the new system. Despite the excellent prospects of crossbar arrays, the biggest obstacle for them is sneak current, which can cause a misreading between the adjacent memory cells, thus resulting in a misoperation in the arrays. The chalcogenide-based ovonic threshold switch (OTS) is a powerful selector with highly nonlinear I-V characteristics that can be used to address the sneak current problem. In this study, we evaluated the electrical characteristics of an OTS with a TiN/GeTe/TiN structure. This device shows nonlinear DC I-V characteristics, an excellent endurance of up to 10⁹ in the burst read measurement, and a stable threshold voltage below 15 mV/dec. In addition, at temperatures below 300 °C, the device exhibits good thermal stability and retains an amorphous structure, which is a strong indication of the aforementioned electrical characteristics.

An Artificial Tactile Neuron Enabling Spiking Representation of Stiffness and Disease Diagnosis

Mechanical properties of biological systems provide useful information about the biochemical status of cells and tissues. Here, an artificial tactile neuron enabling spiking representation of stiffness and spiking neural network (SNN)-based learning for disease diagnosis is reported. An artificial spiking tactile neuron based on an ovonic threshold switch serving as an artificial soma and a piezoresistive sensor as an artificial mechanoreceptor is developed and shown to encode the elastic stiffness of pressed materials into spike frequency evolution patterns. SNN-based learning of ultrasound elastography images abstracted by spike frequency evolution rate enables the classification of malignancy status of breast tumors with a recognition accuracy up to 95.8%. The stiffness-encoding artificial tactile neuron and learning of spiking-represented stiffness patterns hold a great promise for the identification and classification of tumors for disease diagnosis and robot-assisted surgery with low power consumption, low latency, and yet high accuracy.

Sub-Picosecond Non-Equilibrium States in the Amorphous Phase of GeTe Phase-Change Material Thin Films

The sub-picosecond response of amorphous germanium telluride thin film to a femtosecond laser excitation is investigated using frequency-domain interferometry and ab initio molecular dynamics. The time-resolved measurement of the surface dynamics reveals a shrinkage of the film with a dielectric properties' response faster than 300 fs. The systematic ab initio molecular dynamics simulations in non-equilibrium conditions allow the atomic configurations to be retrieved for ionic temperature from 300 to 1100 K and width of the electron distribution from 0.001 to 1.0 eV. Local order of the structures is characterized by in-depth analysis of the angle distribution, phonon modes, and pair distribution function, which evidence a transition toward a new amorphous electronic excited state close in bonding/structure to the liquid state. The results shed a new light on the optically highly excited states in chalcogenide materials involved in both important processes: phase-change materials in memory devices and ovonic threshold switching phenomenon induced by a static field.