Abstract
The missing link between a nonlinear circuit element that is able to self-adjust its conductance according to the history of applied voltage/current and physical realizations of two-terminal oxide-based resistive memory was discovered in early 2008, and has since been intensively studied. This class of memory elements is called memristive devices, which includes resistive random access memories (RRAM), phase change memories (PCM) and spin-transfer torque magnetoresistive memories (STT-MRAM). Memristive devices are mostly CMOS and fab friendly, and promise simpler architecture, higher scalability and stackability (3D), good selectivity, relatively low-power consumption, high endurance and retention, fast operation by utilizing parallelism, and the most important of all, the ability to merge logic and memory. A significantly wide range of resistive switching materials can be categorized under three main redox-related effects, electrochemical metalization effects (ECM), valency change memory effect (VCM) and thermochemical memory effects (TCM). Although, the behavior of these resistive memories can be modeled using high-level finite-state machines (FSMs), the underlying switching mechanisms is yet to be fully understood. Despite the lack of comprehensive understanding of the switching behavior, their application in memory and computing has been constantly improved. These devices can be programmed to exhibit multi-level cell (MLC) and binary cell behavior, thus analog and digital memories can be exists in one device depends on programming. In this chapter, we highlight some of the in situ computational capability of memristive devices.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alibart, F., Zamanidoost, E., Strukov, D.B.: Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat. Commun. 4 (2013)
Backus, J.: Can programming be liberated from the von Neumann style?: A functional style and its algebra of programs. Commun. ACM 21(8), 613–641 (1978)
Bienenstock, E.L., Cooper, L.N., Munro, P.W.: Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J. Neurosci. 2(1), 32–48 (1982)
Borghetti, J., Snider, G., Kuekes, P., Yang, J., Stewart, D., Williams, R.: ‘Memristive’ switches enable ‘stateful’ logic operations via material implication. Nature 464(7290), 873–876 (2010)
Caporale, N., Dan, Y.: Spike timing-dependent plasticity: a Hebbian learning rule. Annu. Rev. Neurosci. 31, 25–46 (2008)
Chang, T., Jo, S.-H., Lu, W.: Short-term memory to long-term memory transition in a nanoscale memristor. ACS Nano 5(9), 7669–7676 (2011)
Chua, L.O.: Memristor—the missing circuit element. IEEE Trans. Circuit Theory 18(5), 507–519 (1971)
Chua, L.O., Kang, S.M.: Memristive devices and systems. Proc. IEEE 64(2), 209–223 (1976)
Hasegawa, T., Ohno, T., Terabe, K., Tsuruoka, T., Nakayama, T., Gimzewski, J., Aono, M.: Learning abilities achieved by a single solid-state atomic switch. Adv. Mater. 22(16), 1831–1834 (2010)
Izhikevich, E.M., Desai, N.S.: Relating STDP to BCM. Neural Comput. 15(7), 1511–1523 (2003)
Jackson, B.L., Rajendran, B., Corrado, G.S., Breitwisch, M., Burr, G.W., Cheek, R., Gopalakrishnan, K., Raoux, S., Rettner, C.T., Padilla, A., et al.: Nanoscale electronic synapses using phase change devices. ACM J. Emerg. Technol. Comput. Syst. 9(2), 12 (2013)
Jeong, D.S., Kim, I., Ziegler, M., Kohlstedt, H.: Towards artificial neurons and synapses: a materials point of view. RSC Adv. 3(10), 3169–3183 (2013)
Jiang, H., Xia, Q.: Effect of voltage polarity and amplitude on electroforming of TiO2 based memristive devices. Nanoscale 5(8), 3257–3261 (2013)
Jo, S., Chang, T., Ebong, I., Bhadviya, B., Mazumder, P., Lu, W.: Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 10(4), 1297–1301 (2010)
Kavehei, O., Iqbal, A., Kim, Y., Eshraghian, K., Al-Sarawi, S., Abbott, D.: The fourth element: characteristics, modelling and electromagnetic theory of the memristor. Proc. R. Soc. A, Math. Phys. Eng. Sci. 466(2120), 2175 (2010)
Kavehei, O., Al-Sarawi, S., Cho, K.-R., Iannella, N., Kim, S.-J., Eshraghian, K., Abbott, D.: Memristor-based synaptic networks and logical operations using in-situ computing. In: International Conference on Intelligent Sensors, Sensor Networks and Information Processing, pp. 137–142 (2011)
Kavehei, O., Cho, K., Lee, S., Kim, S., Al-Sarawi, S., Abbott, D., Eshraghian, K.: Fabrication and modeling of Ag/TiO2/ITO memristor. In: 54th IEEE International Midwest Symposium on Circuits and Systems, pp. 1–4 (2011)
Kavehei, O., Al-Sarawi, S., Cho, K.-R., Eshraghian, K., Abbott, D.: An analytical approach for memristive nanoarchitectures. IEEE Trans. Nanotechnol. 11(2), 374–385 (2012)
Kavehei, O., Cho, K.-R., Lee, S.-J., Al-Sarawi, S., Eshraghian, K., Abbott, D.: Integrated memristor-mos (M2) sensor for basic pattern matching applications. J. Nanosci. Nanotechnol. 13(5), 3638–3640 (2013)
Kavehei, O., Lee, S.-J., Cho, K.-R., Al-Sarawi, S., Abbott, D.: A pulse-frequency modulation sensor using memristive-based inhibitory interconnections. J. Nanosci. Nanotechnol. 13(5), 3505–3510 (2013)
Kavehei, O., Linn, E., Nielen, L., Tappertzhofen, S., Skafidas, E., Valov, I., Waser, R.: An associative capacitive network based on nanoscale complementary resistive switches for memory-intensive computing. Nanoscale 5(11), 5119–5128 (2013)
Kim, K.-H., Gaba, S., Wheeler, D., Cruz-Albrecht, J.M., Hussain, T., Srinivasa, N., Lu, W.: A functional hybrid memristor crossbar-array/CMOS system for data storage and neuromorphic applications. Nano Lett. 12(1), 389–395 (2011)
Li, S., Zeng, F., Chen, C., Liu, H., Tang, G., Gao, S., Song, C., Lin, Y., Guo, D., et al.: Synaptic plasticity and learning behaviours mimicked through Ag interface movement in an Ag/conducting polymer/Ta memristive system. J. Mater. Chem. C 1(34), 5292–5298 (2013)
Lim, H., Jang, H.-W., Lee, D.-K., Kim, I., Hwang, C.S., Jeong, D.S.: Elastic resistance change and action potential generation of non-faradaic Pt/TiO2/Pt capacitors. Nanoscale 5(14), 6363–6371 (2013)
Linn, E., Rosezin, R., Kügeler, C., Waser, R.: Complementary resistive switches for passive nanocrossbar memories. Nat. Mater. 9(5), 403–406 (2010)
Menzel, S., Tappertzhofen, S., Waser, R., Valov, I.: Switching kinetics of electrochemical metallization memory cells. Phys. Chem. Chem. Phys. 15(18), 6945–6952 (2013)
Mott, N., Gurney, R.: Electronic Processes in Ionic Crystals. Dover, New York (1964). Chapter 2
Mouttet, B.: Proposal for memristors in signal processing. Nano-Net 11–13 (2009)
Ohno, T., Hasegawa, T., Tsuruoka, T., Terabe, K., Gimzewski, J.K., Aono, M.: Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10(8), 591–595 (2011)
Ovshinsky, S.R.: Reversible electrical switching phenomena in disordered structures. Phys. Rev. Lett. 21, 1450–1453 (1968)
Ovshinsky, S.R.: The ovonic cognitive computer: a new paradigm. EPCOS Library (2004)
Pershin, Y., Di Ventra, M.: Practical approach to programmable analog circuits with memristors. IEEE Trans. Circuits Syst. I, Regul. Pap. 57(8), 1857–1864 (2010)
Pershin, Y., Di Ventra, M.: Memory effects in complex materials and nanoscale systems. Adv. Phys. 60(2), 145–227 (2011)
Pfeil, T., Potjans, T.C., Schrader, S., Potjans, W., Schemmel, J., Diesmann, M., Meier, K.: Is a 4-bit synaptic weight resolution enough?—constraints on enabling spike-timing dependent plasticity in neuromorphic hardware. Front. Neurosci. 6 (2012)
Pickett, M.D., Strukov, D.B., Borghetti, J.L., Yang, J.J., Snider, G.S., Stewart, D.R., Williams, R.S.: Switching dynamics in titanium dioxide memristive devices. J. Appl. Phys. 106(7), 074508 (2009)
Qureshi, M.S., Pickett, M., Miao, F., Strachan, J.P.: CMOS interface circuits for reading and writing memristor crossbar array. In: IEEE International Symposium on Circuits and Systems (ISCAS), pp. 2954–2957 (2011)
Rosezin, R., Linn, E., Kügeler, C., Bruchhaus, R., Waser, R.: Crossbar logic using bipolar and complementary resistive switches. IEEE Electron Device Lett. 32(6), 710–712 (2011)
Snider, G.S.: Cortical computing with memristive nanodevices. SciDAC Rev. 10, 58–65 (2008)
Snider, G.S.: Instar and outstar learning with memristive nanodevices. Nanotechnology 22, 015201 (2011)
Song, S., Miller, K., Abbott, L.: Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3, 919–926 (2000)
Strukov, D.B., Snider, G.S., Stewart, D.R., Williams, R.S.: The missing memristor found. Nature 453, 80–83 (2008)
Suri, M., Querlioz, D., Bichler, O., Palma, G., Vianello, E., Vuillaume, D., Gamrat, C., DeSalvo, B.: Bio-inspired stochastic computing using binary CBRAM synapses. IEEE Trans. Electron Devices 60(7) (2013)
Thakoor, S., Moopenn, A., Daud, T., Thakoor, A.: Solid-state thin-film memistor for electronic neural networks. J. Appl. Phys. 67(6), 3132–3135 (1990)
Valov, I., Linn, E., Tappertzhofen, S., Schmelzer, S., van den Hurk, J., Lentz, F., Waser, R.: Nanobatteries in redox-based resistive switches require extension of memristor theory. Nat. Commun. 4, 1771 (2013)
Waser, R.: Nanoelectronics and Information Technology. Wiley-VCH, Weinheim (2012)
Waser, R., Aono, M.: Nanoionics-based resistive switching memories. Nat. Mater. 6(11), 833–840 (2007)
Whitehead, A., Russell, B.: Principia Mathematica, vol. 2 (1912)
Widrow, B.: An adaptive ‘ADALINE’ neuron using chemical “memistors”. Stanford Electronics Laboratories Technical Report, Tech. Rep. TR-1553-2, 23 Oct. 1960
Xia, Q., Pickett, M.D., Yang, J.J., Li, X., Wu, W., Medeiros-Ribeiro, G., Williams, R.S.: Two-and three-terminal resistive switches: nanometer-scale memristors and memistors. Adv. Funct. Mater. 21(14), 2660–2665 (2011)
Yang, J.J., Strukov, D.B., Stewart, D.R.: Memristive devices for computing. Nat. Nanotechnol. 8(1), 13–24 (2012)
Yang, J.J., Zhang, M.-X., Pickett, M.D., Miao, F., Strachan, J.P., Li, W.-D., Yi, W., Ohlberg, D.A., Choi, B.J., Wu, W., et al.: Engineering nonlinearity into memristors for passive crossbar applications. Appl. Phys. Lett. 100, 113501 (2012)
Yu, S., Gao, B., Fang, Z., Yu, H., Kang, J., Wong, H.-S.P.: A neuromorphic visual system using RRAM synaptic devices with sub-pJ energy and tolerance to variability: experimental characterization and large-scale modeling. In: IEEE International Electron Devices Meeting, pp. 239–242 (2012)
Zamarreño-Ramos, C., Camuñas-Mesa, L., Pérez-Carrasco, J., Masquelier, T., Serrano-Gotarredona, T., Linares-Barranco, B.: On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex. In: Frontiers in Neuroscience, vol. 5 (2011)
Acknowledgement
This work was supported by an Early Career Researcher grant from the Melbourne School of Engineering, The University of Melbourne.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Kavehei, O., Skafidas, E., Eshraghian, K. (2014). Memristive in Situ Computing. In: Adamatzky, A., Chua, L. (eds) Memristor Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-02630-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-02630-5_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02629-9
Online ISBN: 978-3-319-02630-5
eBook Packages: Computer ScienceComputer Science (R0)