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Operando diagnostic detection of interfacial oxygen ‘breathing’ of resistive random access memory by bulk-sensitive hard X-ray photoelectron spectroscopy
The HfO2-based resistive random access memory (RRAM) is one of the most promising candidates for non-volatile memory applications. The detection and examination of the dynamic behavior of oxygen ions/vacancies are crucial to deeply understand the microscopic physical nature of the resistive switching (RS) behavior. By using synchrotron radiation based, non-destructive and bulk-sensitive hard X-ray photoelectron spectroscopy (HAXPES), we demonstrate an operando diagnostic detection of the oxygen ‘breathing’ behavior at the oxide/metal interface, namely, oxygen migration between HfO2 and TiN during different RS periods. The results highlight the significance of oxide/metal interfaces in RRAM, even in filament-type devices. IMPACT STATEMENT: The oxygen ‘breathing’ behavior at the oxide/metal interface of filament-type resistive random access memory devices is operandoly detected using hard X-ray photoelectron spectroscopy as a diagnostic tool. © 2019, © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Geometric conductive filament confinement by nanotips for resistive switching of HfO2-RRAM devices with high performance
Filament-type HfO2-based RRAM has been considered as one of the most promising candidates for future non-volatile memories. Further improvement of the stability, particularly at the “OFF” state, of such devices is mainly hindered by resistance variation induced by the uncontrolled oxygen vacancies distribution and filament growth in HfO2 films. We report highly stable endurance of TiN/Ti/HfO2/Si-tip RRAM devices using a CMOS compatible nanotip method. Simulations indicate that the nanotip bottom electrode provides a local confinement for the electrical field and ionic current density; thus a nano-confinement for the oxygen vacancy distribution and nano-filament location is created by this approach. Conductive atomic force microscopy measurements confirm that the filaments form only on the nanotip region. Resistance switching by using pulses shows highly stable endurance for both ON and OFF modes, thanks to the geometric confinement of the conductive path and filament only above the nanotip. This nano-engineering approach opens a new pathway to realize forming-free RRAM devices with improved stability and reliability.
Modulating the Filamentary-Based Resistive Switching Properties of HfO2 Memristive Devices by Adding Al2O3 Layers
The resistive switching properties of HfO2 based 1T-1R memristive devices are electrically modified by adding ultra-thin layers of Al2 O3 into the memristive device. Three different types of memristive stacks are fabricated in the 130 nm CMOS technology of IHP. The switching properties of the memristive devices are discussed with respect to forming voltages, low resistance state and high resistance state characteristics and their variabilities. The experimental I–V characteristics of set and reset operations are evaluated by using the quantum point contact model. The properties of the conduction filament in the on and off states of the memristive devices are discussed with respect to the model parameters obtained from the QPC fit.
Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices
Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper for the on-chip recognition of saliva samples of COPD patients and healthy controls. Results of its performance evaluations showed that the digital neuromorphic chip is capable of recognizing unseen COPD samples with accuracy and sensitivity values of 89% and 86%, respectively. Integration of this technology into personalized healthcare devices will enable the better management of chronic diseases such as COPD. © 2020, The Author(s).
Toward Reliable Compact Modeling of Multilevel 1T-1R RRAM Devices for Neuromorphic Systems
In this work, three different RRAM compact models implemented in Verilog-A are analyzed and evaluated in order to reproduce the multilevel approach based on the switching capability of experimental devices. These models are integrated in 1T-1R cells to control their analog behavior by means of the compliance current imposed by the NMOS select transistor. Four different resistance levels are simulated and assessed with experimental verification to account for their multilevel capability. Further, an Artificial Neural Network study is carried out to evaluate in a real scenario the viability of the multilevel approach under study.
Material insights of HfO2-based integrated 1-transistor-1-resistor resistive random access memory devices processed by batch atomic layer deposition
With the continuous scaling of resistive random access memory (RRAM) devices, in-depth understanding of the physical mechanism and the material issues, particularly by directly studying integrated cells, become more and more important to further improve the device performances. In this work, HfO2-based integrated 1-transistor-1-resistor (1T1R) RRAM devices were processed in a standard 0.25 μm complementary-metal-oxide-semiconductor (CMOS) process line, using a batch atomic layer deposition (ALD) tool, which is particularly designed for mass production. We demonstrate a systematic study on TiN/Ti/HfO2/TiN/Si RRAM devices to correlate key material factors (nano-crystallites and carbon impurities) with the filament type resistive switching (RS) behaviours. The augmentation of the nano-crystallites density in the film increases the forming voltage of devices and its variation. Carbon residues in HfO2 films turn out to be an even more significant factor strongly impacting the RS behaviour. A relatively higher deposition temperature of 300 °C dramatically reduces the residual carbon concentration, thus leading to enhanced RS performances of devices, including lower power consumption, better endurance and higher reliability. Such thorough understanding on physical mechanism of RS and the correlation between material and device performances will facilitate the realization of high density and reliable embedded RRAM devices with low power consumption.