Low‐Frequency‐Noise Spectroscopy of TaOx‐based Resistive Switching Memory

Abstract

Research and development into resistive switching memories, such as resistive random access memory (ReRAM) and memristors, are being actively promoted toward the realization of new computing techniques. To improve the reliability of these devices, it is important to clarify their resistance characteristics. Low‐frequency‐noise spectroscopy (LFNS) is a powerful method for investigating the nature of traps in conduction paths, even at the nanoscale. In this article, the results of LFNS measurements on a TaOx‐based ReRAM device are presented. The temperature dependence of the noise spectra at given frequencies reveals the existence of multiple trap levels, which are observed over a wide range of resistance values. These experimental results show that the ReRAM device is particularly advantageous when used in analog resistive switching applications.

2D Heterostructure of Bi2O2Se/Bi2SeOx Nanosheet for Resistive Random Access Memory

“…With these excellent characteristics, RRAM has been attracting more and more research interest in the field of non‐volatile data storage, logic operations and neuromorphic computing. [ 4–8 ] RRAM is a simple two‐terminal configuration with a vertical sandwiched metal/insulator/metal (MIM) structure. [ 9,10 ]…”

“…With these excellent characteristics, RRAM has been attracting more and more research interest in the field of non-volatile data storage, logic operations and neuromorphic computing. [4][5][6][7][8] RRAM is a simple two-terminal configuration with a vertical sandwiched metal/insulator/metal (MIM) structure. [9,10] the resistive switching behavior of 2D Bi 2 O 2 Se was observed, making it potential for the application of Bi 2 O 2 Se in RRAM.…”

Operando observation of analog resistance change in a buried metal/oxide interface by a laser-excited photoemission electron microscope

“…We deem such scenario to be applicable not only for operations in the whole of the device area but also for operations in the local area as reported previously. 25,31) With the capability of this method on nondestructive operando carrier observation, we expect its application to improve the device reliability where inhomogeneity plays a crucial role.…”

Self‐Curable Synaptic Ferroelectric FET Arrays for Neuromorphic Convolutional Neural Network

“…LFN spectroscopy is an established diagnostic tool to evaluate the reliability of semiconductor materials and devices, including FeFETs. [40][41][42][43][44] Unlike other electrical measurements, such as current-voltage characterization and the charge pumping method, LFN spectroscopy measures not only the defects at the gate oxide-channel interface but also the bulk defects. In FETs, LFN is generated from the carrier trapping/de-trapping processes to/from defects in the gate oxide, and the defect density can be quantitatively characterized by measuring the power spectral density (PSD) of all devices.…”