Redox-based memristive devices : towards highly scalable synaptic electronics

Complimentary Metal-Oxide Semiconductor (CMOS)-based systems have been the core elements of the semiconductor technology for decades. With the predicted CMOS scaling limit and the increasing amount of data in today’s technology, researchers around the world have started looking for emerging electronics to keep up with the hardware requirements and new radical computing paradigm, e.g., quantum and neuromorphic computing, to further lower the computational cost, especially in handling unstructured data set where the conventional von Neumann architecture struggles to strike a balance between power cost and space trade-off. Redox-based memristive devices emerge as one of the promising candidates to fulfil the hardware requirements of the emerging neuromorphic computing systems, e.g., as a synaptic device element. The highly scalable nature of the device along with its analog characteristic have been the focus of the research in the field. However, the inherent stochasticity, non-linearity, and symmetry of the device conductance switching behaviour hinder its progress in synaptic device applications. Fortunately, the synaptic device requirements are highly dependent on the target applications. Thus, systematic and thorough understanding upon the device physics involve during the switching operation is required to have full control on the performance at the system level and how to further improve it. This thesis focuses on the development of redox-based memristive devices governed by different underlying physical mechanisms, i.e., anion and cation-based system, to facilitate different device applications. The anion-based devices were operated under different mode of programming to investigate its potential application in different synaptic array architectures. The switching dynamics, under trap-controlled space-charge-limited mechanism, and its correlation with the linearity and symmetry of the device conductance response are extensively discussed. On the other hand, the cation-based devices were operated under volatile switching regime to investigate its unique switching dynamics for highly scalable select devices. The device temporal response to external voltage applied was used to understand the device switching behaviour under the theoretical framework of field-induced nucleation theory and Rayleigh instability.Doctor of Philosoph

Fabrication and characterisation of MgO-based redox random access memory

Emerging memory technologies must low in energy consumption, easily integrable into high density memory architecture, with high scalability, fast operating speed, high endurance capability, and long retention time. Due to this requirement, two terminal memory cells are getting more and more attention in recent years. Redox Random Access Memory is one of the most promising two terminal memory candi- date for the future memory technology. In this work, fabrication and characterisa- tion of Magnesium Oxide-based ReRAM device is extensively study. Magnesium oxide (MgO) has been one of the most preferred materials for MOS, MTJ, and spin valves applications. It has a considerably low formation of interfacial layers for deposition, a high thermal conductivity and breakdown field, large band gap, and a high dielectric constant, subject to the fabrication process. These proper- ties enhance the potential of MgO thin films to establish sufficient band offsets and to minimise the presences of leakage currents in device applications. Several studies have shown the potential of MgO in ReRAM application. However, the MgO-based ReRAM device has been known to have a considerably high operating voltage that lead to high energy consumption. In this study, the change subject to fabrication parameters such as post-deposition annealing and introduction of dopants are studied to enhance the performance of MgO-based ReRAM. Further- more, the dominant conduction mechanisms for the high and low resistance states of the device are investigated, which suggest a great possible improvement can be done in the scalability aspect of the device.Bachelor of Science in Physic

Oxide-based RRAM materials for neuromorphic computing

In this review, a comprehensive survey of different oxide-based resistive random-access memories (RRAMs) for neuromorphic computing is provided. We begin with the history of RRAM development, physical mechanism of conduction, fundamental of neuromorphic computing, followed by a review of a variety of RRAM oxide materials (PCMO, HfOx, TaOx, TiOx, NiOx, etc.) with a focus on their application for neuromorphic computing. Our goal is to give a broad review of oxide-based RRAM materials that can be adapted to neuromorphic computing and to help further ongoing research in the field.NRF (Natl Research Foundation, S’pore

Frequency-dependent synapse weight tuning in 1S1R with short-term plasticity TiOx-based exponential selector

Short-term plasticity (STP) is an important synaptic characteristic in the hardware implementation of artificial neural networks (ANN), as it enables the temporal information processing (TIP) capability. However, the STP feature is rather challenging to reproduce from a single non-volatile resistive random-access memory (RRAM) element, as it requires a certain degree of volatility. In this work, a Pt/TiOx/Pt exponential selector is introduced not only to suppress the sneak current, but also to enable the TIP feature in a one selector-one RRAM (1S1R) synaptic device. Our measurements reveal that the exponential selector exhibits the STP characteristic, while a Pt/HfOx/Ti RRAM enables the long-term memory capability of the synapse. Thereafter, we experimentally demonstrated pulse frequency-dependent multilevel switching in the 1S1R device, exhibiting the TIP capability of the developed 1S1R synapse. The observed STP of the selector is strongly influenced by the bottom metal-oxide interface, in which Ar plasma treatment on the bottom Pt electrode show the annihilation of the STP feature in the selector. A mechanism is thus proposed to explain the observed STP, using the local electric field enhancement induced at the metal-oxide interface coupled with the drift-diffusion model of mobile O2- and Ti3+ ions. This work therefore provides a reliable means of producing the STP feature in a 1S1R device, which demonstrates the TIP capability sought after in hardware-based ANN.Agency for Science, Technology and Research (A*STAR)Submitted/Accepted versionThis work was supported by a RIE2020 ASTAR AME IAF-ICP Grant (No.I1801E0030)

Unidirectional threshold switching induced by Cu migration with high selectivity and ultralow OFF current under gradual electroforming treatment

A gradual electroforming process was implemented on the pristine Pt/HfOx/Cu/Pt structure to realize volatile threshold switching characteristics of a diffusive memristor. The reported devices exhibit stable unidirectional threshold switching properties with high selectivity of >107 and ultralow OFF current of ∼100 fA for over 104 endurance cycles. Nucleation theory on spheroidal-shaped metallic filament growth is used to extensively discuss the structural changes of the device after gradual forming treatments by analyzing the applied bias amplitude dependency of the finite delay time required by the device to turn ON under external electric field. On the other hand, the Rayleigh instability model was implemented for the aforementioned spheroidal metallic nucleus to explain the relaxation dynamics of the device. It was shown that the relaxation time of the device depends on the initial profile of the nucleus within the insulating layer. The broadening of the ON current distribution of the device was observed during the device endurance test. This is correlated to the presence of a random telegraph signal (RTS) during the ON state of the device.ASTAR (Agency for Sci., Tech. and Research, S’pore)Accepted versio

Conduction mechanisms on high retention annealed mgo-based resistive switching memory devices

We report on the conduction mechanisms of novel Ru/MgO/Cu and Ru/MgO/Ta resistive switching memory (RSM) devices. Current-voltage (I–V) measurements revealed Schottky emission (SE) as the dominant conduction mechanism in the high resistance state (HRS), which was validated by varying temperatures and transmission electron microscopy (TEM) results. Retention of more than 10 years at 85 °C was obtained for both Ru/MgO/Ta and Ru/MgO/Cu RSM devices. In addition, annealing processes greatly improved the consistency of HRS and LRS switching paths from cycle to cycle, exhibiting an average ON/OFF ratio of 102. Further TEM studies also highlighted the difference in crystallinity between different materials in Ru/MgO/Cu RSM devices, confirming Cu filament identification which was found to be 10 nm in width.Published versio

Strain-induced degradation and recovery of flexible NbOx-based threshold switching device

Abstract We investigate the functionality of NbOx-based selector devices on a flexible substrate. It was observed that the failure mechanism of cyclic tensile strain is from the disruption of atom arrangements, which essentially led to the crack formation of the film. When under cyclic compressive strain, buckling delamination of the film occurs as the compressed films have debonded from their neighboring layers. By implementing an annealing process after the strain-induced degradation, recovery of the device is observed with reduced threshold and hold voltages. The physical mechanism of the device is investigated through Poole–Frenkel mechanism fitting, which provides insights into the switching behavior after mechanical strain and annealing process. The result demonstrates the potential of the NbOx device in flexible electronics applications with a high endurance of up to 105 cycles of cyclic bending strain and the recovery of the device after degradation

Proton-assisted redox-based three-terminal memristor for synaptic device applications

Emerging technologies, i.e., spintronics, 2D materials, and memristive devices, have been widely investigated as the building block of neuromorphic computing systems. Three-terminal memristor (3TM) is specifically designed to mitigate the challenges encountered by its two-terminal counterpart as it can concurrently execute signal transmission and memory operations. In this work, we present a complementary metal-oxide-semiconductor-compatible 3TM with highly linear weight update characteristics and a dynamic range of ∼15. The switching mechanism is governed by the migration of oxygen ions and protons in and out of the channel under an external gate electric field. The involvement of the protonic defects in the electrochemical reactions is proposed based on the bipolar pulse trains required to initiate the oxidation process and the device electrical characteristics under different humidity levels. For the synaptic operation, an excellent endurance performance with over 256k synaptic weight updates was demonstrated while maintaining a stable dynamic range. Additionally, the synaptic performance of the 3TM is simulated and implemented into a four-layer neural network (NN) model, achieving an accuracy of ∼92% in MNIST handwritten digit recognition. With such desirable conductance modulation characteristics, our proposed 3T-memristor is a promising synaptic device candidate to realize the hardware implementation of the artificial NN.Agency for Science, Technology and Research (A*STAR)This work was supported by a RIE2020 ASTAR AME IAF-ICP grant (no. I1801E0030)

The impact of oxygen vacancy defect density on the nonlinearity and short-term plasticity of TiO₂-based exponential selector

The readout margin of the one selector-one RRAM crossbar array architecture is strongly dependent on the nonlinearity of the selector device. In this work, we demonstrated that the nonlinearity of Pt/TiO2/Pt exponential selectors increases with decreasing oxygen vacancy defect density. The defect density is controlled by modulating the sputtering pressure in the oxide deposition process. Our results reveal that the dominant conduction mechanisms of the Pt/TiO2/Pt structure transit from Schottky emission to Poole-Frenkel emission with the increase of sputtering pressure. Such transition is attributed to the rise of oxygen vacancy concentration. In addition, the short-term plasticity feature of the Pt/TiO2/Pt selector is shown to be enhanced with a lower defect density. These results suggest that low defect density is necessary for improved exponential selector performances.Agency for Science, Technology and Research (A*STAR)This work was supported by a RIE2020 ASTAR AME IAFICP Grant (No. I1801E0030)

Exploring the impact of variability in resistance distributions of RRAM on the prediction accuracy of deep learning neural networks

In this work, we explore the use of the resistive random access memory (RRAM) device as a synapse for mimicking the trained weights linking neurons in a deep learning neural network (DNN) (AlexNet). The RRAM devices were fabricated in-house and subjected to 1000 bipolar read-write cycles to measure the resistances recorded for Logic-0 and Logic-1 (we demonstrate the feasibility of achieving eight discrete resistance states in the same device depending on the RESET stop voltage). DNN simulations have been performed to compare the relative error between the output of AlexNet Layer 1 (Convolution) implemented with the standard backpropagation (BP) algorithm trained weights versus the weights that are encoded using the measured resistance distributions from RRAM. The IMAGENET dataset is used for classification purpose here. We focus only on the Layer 1 weights in the AlexNet framework with 11 × 11 × 96 filters values coded into a binary floating point and substituted with the RRAM resistance values corresponding to Logic-0 and Logic-1. The impact of variability in the resistance states of RRAM for the low and high resistance states on the accuracy of image classification is studied by formulating a look-up table (LUT) for the RRAM (from measured I-V data) and comparing the convolution computation output of AlexNet Layer 1 with the standard outputs from the BP-based pre-trained weights. This is one of the first studies dedicated to exploring the impact of RRAM device resistance variability on the prediction accuracy of a convolutional neural network (CNN) on an AlexNet platform through a framework that requires limited actual device switching test data.Agency for Science, Technology and Research (A*STAR)Economic Development Board (EDB)National Research Foundation (NRF)Published versionThis research was funded by A*STAR BRENAIC Research Project No. A18A5b0056 and the APC associated with the publication as well. Funding support for fabrication and characterization of devices were provided by the Economic Development Board EDB-IPP (RCA – 16/216) program and the Industry-IHL Partnership Program (NRF2015-IIP001-001)