A C-Te-based binary OTS device exhibiting excellent performance and high thermal stability for selector application.

In this letter, we demonstrate a new binary ovonic threshold switching (OTS) selector device scalable down to ø30 nm based on C-Te. Our proposed selector device exhibits outstanding performance such as a high switching ratio (Ion/Ioff > 105), an extremely low off-current (∼1 nA), an extremely fast operating speed of <10 ns (transition time of <2 ns and delay time of <8 ns), high endurance (109), and high thermal stability (>450 °C). The observed high thermal stability is caused by the relatively small atomic size of C, compared to Te, which can effectively suppress the segregation and crystallization of Te in the OTS film. Furthermore, to confirm the functionality of the selector in a crossbar array, we evaluated a 1S-1R device by integrating our OTS device with a ReRAM (resistive random access memory) device. The 1S-1R integrated device exhibits a successful suppression of leakage current at the half-selected cell and shows an excellent read-out margin (>212 word lines) in a fast read operation.

Effect of conductance linearity and multi-level cell characteristics of TaOx-based synapse device on pattern recognition accuracy of neuromorphic system.

To improve the classification accuracy of an image data set (CIFAR-10) by using analog input voltage, synapse devices with excellent conductance linearity (CL) and multi-level cell (MLC) characteristics are required. We analyze the CL and MLC characteristics of TaOx-based filamentary resistive random access memory (RRAM) to implement the synapse device in neural network hardware. Our findings show that the number of oxygen vacancies in the filament constriction region of the RRAM directly controls the CL and MLC characteristics. By adopting a Ta electrode (instead of Ti) and the hot-forming step, we could form a dense conductive filament. As a result, a wide range of conductance levels with CL is achieved and significantly improved image classification accuracy is confirmed.

Controllable quantized conductance for multilevel data storage applications using conductive bridge random access memory.

In this paper, we investigate the quantized conduction behavior of conductive bridge random access memory (CBRAM) with varied materials and ramping rates. We report stable and reproducible quantized conductance states with integer multiples of fundamental conductance obtained by optimizing the voltage ramping rate and the Ti-diffusion barrier (DB) at the Cu/HfO2 interface. Owing to controlled diffusion of Cu ions by the Ti-DB and the optimized ramping rate, through which it was possible to control the time delay of Cu ion reduction, more than seven levels of discrete conductance states were clearly observed. Analytical modeling was performed to determine the rate-limiting step in filament growth based on an electrochemical redox reaction. Our understanding of the fundamental mechanisms of quantized conductance behaviors provide a promising future for the multi-bit CBRAM device.