System Maintenance:
There may be intermittent impact on performance while updates are in progress. We apologize for the inconvenience.
By Topic

Circuits and Systems Magazine, IEEE

Issue 2 • Date Secondquarter 2013

Filter Results

Displaying Results 1 - 12 of 12
  • [Front Cover]

    Publication Year: 2013 , Page(s): C1
    Save to Project icon | Request Permissions | PDF file iconPDF (897 KB)  
    Freely Available from IEEE
  • [Table of Contents]

    Publication Year: 2013 , Page(s): 1 - 2
    Save to Project icon | Request Permissions | PDF file iconPDF (530 KB)  
    Freely Available from IEEE
  • Editorial Board

    Publication Year: 2013 , Page(s): 2
    Save to Project icon | Request Permissions | PDF file iconPDF (209 KB)  
    Freely Available from IEEE
  • [From the Guest Editors]

    Publication Year: 2013 , Page(s): 4 - 6
    Save to Project icon | Request Permissions | PDF file iconPDF (739 KB) |  | HTML iconHTML  
    Freely Available from IEEE
  • ISCAS 2014

    Publication Year: 2013 , Page(s): 7
    Save to Project icon | Request Permissions | PDF file iconPDF (1526 KB)  
    Freely Available from IEEE
  • The First Radios Were Made Using Memristors!

    Publication Year: 2013 , Page(s): 8 - 16
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4287 KB) |  | HTML iconHTML  

    In 2008, Williams et al. reported the discovery of the fourth fundamental passive circuit element, memristor, which exhibits electrically controllable state-dependent resistance [1]. We show that one of the first wireless radio detector, called cat's whisker, also the world's first solid-state diode, had memristive properties. We have identified the state variable governing the resistance state of the device and can program it to switch between multiple stable resistance states. Our observations and results are valid for a larger class of devices called coherers, which include the cat's whisker. These devices constitute the missing canonical physical implementations for a memristor (ref. Fig. 1). View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • The Desired Memristor for Circuit Designers

    Publication Year: 2013 , Page(s): 17 - 22
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1839 KB) |  | HTML iconHTML  

    Memristors are two-terminal devices with varying resistance, where the behavior is dependent on the history of the device. In recent years, different physical phenomena of resistive switching have been linked with the theoretical concept of a memristor, and several emerging memory devices (e.g., Phase Change Memory, Resistive RAM, STT-MRAM) are now considered as memristors. Memristors hold promise for use in diverse applications such as memory, digital logic, analog circuits, and neuromorphic systems. Important characteristics of memristors include high speed, low power, good scalability, data retention, endurance, and compatibility with conventional CMOS in terms of manufacturing and operating voltages. One interesting property of some memristors is a nonlinear response to current or voltage. Nonlinear memristors exhibit a current or voltage threshold, such that the resistance is affected only by currents or voltages which exceed the threshold, while the resistance of a linear memristor changes with small perturbations in device current. Different applications exploit different characteristics of a memristor. In this article, the desired characteristics for different applications are presented from the viewpoint of an integrated circuit designer. Understanding the desired characteristics for different applications can assist device and material engineers in providing the appropriate behavior when developing memristive devices, thereby optimizing these devices for different applications. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Applications of Multi-Terminal Memristive Devices: A Review

    Publication Year: 2013 , Page(s): 23 - 41
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4079 KB) |  | HTML iconHTML  

    Memristive devices have the potential for a complete renewal of the electron devices landscape, including memory, logic and sensing applications. This is especially true when considering that the memristive functionality is not limited to two-terminal devices, whose practical realization has been demonstrated within a broad range of different technologies. For electron devices, the memristive functionality can be generally attributed to a state modification, whose dynamics can be engineered to target a specific application. In this review paper, we show examples of two-terminal Resistive RAMs (ReRAM) for standalone memory and Field Programmable Gate Arrays (FPGA) applications. Moreover, a Generic Memory Structure (GMS) utilizing two ReRAMs for 3D-FPGA is discussed. In addition, we show that trap charging dynamics can explain some of the memristive effects previously reported for Schottky-barrier field-effect Si nanowire transistors (SB SiNW FETs). Moreover, the SB SiNW FETs do show additional memristive functionality due to trap charging at the metal/semiconductor surface. The combination of these two memristive effects into multi-terminal MOSFET devices gives rise to new opportunities for both memory and logic applications as well as new sensors based on the physical mechanism that originate memristance. Finally, the multi-terminal memristive devices presented here have the potential of a very high integration density, and they are suitable for hybrid CMOS co-fabrication with a CMOS-compatible process.I. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Compact Circuit Model and Hardware Emulation for Floating Memristor Devices

    Publication Year: 2013 , Page(s): 42 - 55
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2534 KB) |  | HTML iconHTML  

    A compact circuit model and physical hardware emulation for floating memristors are presented. By utilizing memristor 'resistance' as a state variable, and constructing a hardware emulator using a low-complexity modular structure, the model-based emulation can replicate diverse behaviors of different device types. Our hardware emulator for a voltage-actuated, threshold sensitive, two-terminal, floating memristor demonstrates experimentally memristor dynamics. The emulator is capable of computing any arithmetic operations without any disturbance associated with composition of modular structures. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Building Neuromorphic Circuits with Memristive Devices

    Publication Year: 2013 , Page(s): 56 - 73
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (7444 KB) |  | HTML iconHTML  

    The rapid, exponential growth of modern electronics has brought about profound changes to our daily lives. However, maintaining the growth trend now faces significant challenges at both the fundamental and practical levels [1]. Possible solutions include More Moore?developing new, alternative device structures and materials while maintaining the same basic computer architecture, and More Than Moore?enabling alternative computing architectures and hybrid integration to achieve increased system functionality without trying to push the devices beyond limits. In particular, an increasing number of computing tasks today are related to handling large amounts of data, e.g. image processing as an example. Conventional von Neumann digital computers, with separate memory and processer units, become less and less efficient when large amount of data have to be moved around and processed quickly. Alternative approaches such as bio-inspired neuromorphic circuits, with distributed computing and localized storage in networks, become attractive options [2]?[6]. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Memristor Model Comparison

    Publication Year: 2013 , Page(s): 89 - 105
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5572 KB) |  | HTML iconHTML  

    Since the 2008-dated discovery of memristor behavior at the nano-scale, Hewlett Packard is credited for, a large deal of efforts have been spent in the research community to derive a suitable model able to capture the nonlinear dynamics of the nano-scale structures. Despite a considerable number of models of different complexity have been proposed in the literature, there is an ongoing debate over which model should be universally adopted for the investigation of the unique opportunities memristors may offer in integrated circuit design. In order to shed some light into this passionate discussion, this paper compares some of the most noteworthy memristor models present in the literature. The strength of the Pickett?s model stands in its experiment-based development and in its ability to describe some physical mechanism at the origin of memristor dynamics. Since its parameter values depend on the excitation of the memristor and/or on the circuit employing the memristor, it may be assumed as a reference for comparison only in those scenarios for which its parameters were reported in the literature. In this work various noteworthy memristor models are fitted to the Pickett's model under one of such scenarios. This study shows how three models, Biolek's model, the Boundary Condition Memristor model and the Threshold Adaptive Memristor model, outperform the others in the replica of the dynamics observed in the Pickett's model. In the second part of this work the models are used in a couple of basic circuits to study the variance between the dynamical behaviors they give rise to. This analysis intends to make the circuit designers aware of the different behaviors which may occur in memristor-based circuits according to the memristor model under use. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Proposal for Hybrid Memristor-CMOS Spiking Neuromorphic Learning Systems

    Publication Year: 2013 , Page(s): 74 - 88
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3759 KB) |  | HTML iconHTML  

    Recent research in nanotechnology has led to the practical realization of nanoscale devices that behave as memristors, a device that was postulated in the seventies by Chua based on circuit theoretical reasonings. On the other hand, neuromorphic engineering, a discipline that implements physical artifacts based on neuroscience knowledge, has related neural learning mechanisms to the operation of memristors. As a result, neuro-inspired learning architectures can be proposed that exploit nanoscale memristors for building very large scale systems with very dense synaptic-like memory elements. At present, the deep understanding of the internal mechanisms governing memristor operation is still an open issue, and the practical realization of very large scale and reliable ?memristive fabric? for neural learning applications is not a reality yet. However, in the meantime, researchers are proposing and analyzing potential circuit architectures that would combine a standard CMOS substrate with a memristive nanoscale fabric on top to realize hybrid memristor-CMOS neural learning systems. The focus of this paper is on one such architecture for implementing the very well established Spike-Timing-Dependent-Plasticity (STDP) learning mechanism found in biology. In this paper we quickly review spiking neural systems, STDP learning, and memristors, and propose a hybrid memristor-CMOS system architecture with the potential of implementing a large scale STDP learning spiking neural system. Such architecture would eventually allow to implement real-time brain-like processing learning systems with about neurons and synapses on one single Printed Circuit Board (PCB). View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

Aims & Scope

Insofar as the technical articles presented in the proposed magazine, the plan is to cover the subject areas represented by the Society's transactions, including: analog, passive, switch capacitor, and digital filters; electronic circuits, networks, graph theory, and RF communication circuits; system theory; discrete, IC, and VLSI circuit design; multidimensional circuits and systems; large-scale systems and power networks; nonlinear circuits and systems, wavelets, filter banks, and applications; neural networks; and signal processing.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Chi K. Tse
Hong Kong Polytechnic University