Zhuo-Rui Wang

Functionally Complete Boolean Logic in 1T1R Resistive Random Access Memory

Nonvolatile stateful logic through RRAM is a promising route to build in-memory computing architecture. In this letter, a logic methodology based on 1T1R structure has been proposed to implement functionally complete Boolean logics. Arbitrary logic functions could be realized in two steps: initialization and writing. An additional read step is required to read out the logic result, which is in situ stored in the nonvolatile resistive state of the memory. Cascade problem in building larger logic circuits is also discussed. Our 1T1R logic device and operation method could be beneficial for massive integration and practical application of RRAM-based logic.

Control of Synaptic Plasticity Learning of Ferroelectric Tunnel Memristor by Nanoscale Interface Engineering

Brain-inspired computing is an emerging field, which intends to extend the capabilities of information technology beyond digital logic. The progress of the field relies on artificial synaptic devices as the building block for brainlike computing systems. Here, we report an electronic synapse based on a ferroelectric tunnel memristor, where its synaptic plasticity learning property can be controlled by nanoscale interface engineering. The effect of the interface engineering on the device performance was studied. Different memristor interfaces lead to an opposite virgin resistance state of the devices. More importantly, nanoscale interface engineering could tune the intrinsic band alignment of the ferroelectric/metal-semiconductor heterostructure over a large range of 1.28 eV, which eventually results in different memristive and spike-timing-dependent plasticity (STDP) properties of the devices. Bidirectional and unidirectional gradual resistance modulation of the devices could therefore be controlled by tuning the band alignment. This study gives useful insights on tuning device functionalities through nanoscale interface engineering. The diverse STDP forms of the memristors with different interfaces may play different specific roles in various spike neural networks.

Memcomputing: fusion of memory and computing

In this world of ubiquitous computing, the contemporary electronic digital computer has become an essential machine that is available anytime and everywhere. Computing systems come in various forms, including desktops, laptops, tablets, mobile phones, smart watches, and other daily life devices. These systems all originate from the earliest huge, heavy EDVAC and UNIVAC machines of the 1940s, and all share a universal architecture conceived by von Neumann, who divided the computing system into five primary groups: a central arithmetic part (CA), a central control part (CC), memory (M) and outside recording medium (R), input, and output. The CA and CC parts evolved into the central processing unit (CPU), whereas the M and R correspond to the high-speed main memory that stores data and instructions (SRAM and DRAM) and external mass storage, respectively.

Reconfigurable logic in nanosecond Cu/GeTe/TiN filamentary memristors for energy-efficient in-memory computing

Owing to the capability of integrating the information storage and computing in the same physical location, in-memory computing with memristors has become a research hotspot as a promising route for non von Neumann architecture. However, it is still a challenge to develop high performance devices as well as optimized logic methodologies to realize energy-efficient computing. Herein, filamentary Cu/GeTe/TiN memristor is reported to show satisfactory properties with nanosecond switching speed (<60 ns), low voltage operation (<2 V), high endurance (>104 cycles) and good retention (>104 s @85 °C). It is revealed that the charge carrier conduction mechanisms in high resistance and low resistance states are Schottky emission and hopping transport between the adjacent Cu clusters, respectively, based on the analysis of current-voltage behaviors and resistance-temperature characteristics. An intuitive picture is given to describe the dynamic processes of resistive switching. Moreover, based on the basic material implication (IMP) logic circuit, we proposed a reconfigurable logic method and experimentally implemented IMP, NOT, OR, and COPY logic functions. Design of a one-bit full adder with reduction in computational sequences and its validation in simulation further demonstrate the potential practical application. The results provide important progress towards understanding of resistive switching mechanism and realization of energy-efficient in-memory computing architecture.

Theoretical investigation of the Ag filament morphology in conductive bridge random access memories

Conductive bridge random access memories (CBRAMs) usually involve active Ag or Cu metals, where the formation of metal filaments accounts for the low resistance state. For the application of neuromorphic computation, it is highly desirable to develop artificial neurons and synapses, which utilize the complicated volatile or nonvolatile resistive switching phenomena, respectively. This can be achieved by controlling the morphology and stability of the filaments, which requires a deep understanding of the filament formation and disruption mechanisms. Using ab initio calculations, we explored the physical mechanism behind various Ag filament morphologies and growth modes, using GeSe, ZrO2, SiO2, and a-Si as the examples. The roles of Ag and Ag+ stability inside the dielectric, the migration barrier of Ag+, and the Ag+ solvation effect have been investigated in detail. A comprehensive model has been proposed, which in particular could explain the diverse Ag filament morphology experimentally observed in sputtered SiO2 and PECVD SiO2. Our theoretical approach can serve as a pre-screening method in designing new solid-state electrolyte materials of CBRAM, aiming at new functionalities in neuromorphic computation or in-memory logic computing.Conductive bridge random access memories (CBRAMs) usually involve active Ag or Cu metals, where the formation of metal filaments accounts for the low resistance state. For the application of neuromorphic computation, it is highly desirable to develop artificial neurons and synapses, which utilize the complicated volatile or nonvolatile resistive switching phenomena, respectively. This can be achieved by controlling the morphology and stability of the filaments, which requires a deep understanding of the filament formation and disruption mechanisms. Using ab initio calculations, we explored the physical mechanism behind various Ag filament morphologies and growth modes, using GeSe, ZrO2, SiO2, and a-Si as the examples. The roles of Ag and Ag+ stability inside the dielectric, the migration barrier of Ag+, and the Ag+ solvation effect have been investigated in detail. A comprehensive model has been proposed, which in particular could explain the diverse Ag filament morphology experimentally observed in sputt...

Correlation analysis between the current fluctuation characteristics and the conductive filament morphology of HfO2-based memristor

Memristors are attracting considerable interest for their prospective applications in nonvolatile memory, neuromorphic computing, and in-memory computing. However, the nature of resistance switching is still under debate, and current fluctuation in memristors is one of the critical concerns for stable performance. In this work, random telegraph noise (RTN) as the indication of current instabilities in distinct resistance states of the Pt/Ti/HfO2/W memristor is thoroughly investigated. Standard two-level digital-like RTN, multilevel current instabilities with non-correlation/correlation defects, and irreversible current transitions are observed and analyzed. The dependence of RTN on the resistance and read bias reveals that the current fluctuation depends strongly on the morphology and evolution of the conductive filament composed of oxygen vacancies. Our results link the current fluctuation behaviors to the evolution of the conductive filament and will guide continuous optimization of memristive devices.