Fabrication, characterization, and modeling of memristor devices

This paper describes the fabrication of memristor devices based on titanium and hafnium oxides. The device cross sectional area is varied to observe the impact this has on the current-voltage characteristic. A modeling technique is then utilized that is capable of matching the current-voltage characteristics of memristor devices. The model was able to match the titanium oxide device described in this paper with 13.58% error. The device model was then used in a neuromorphic simulation showing that a circuit based on this device is capable of learning logic functions.

[1]  Chris Yakopcic,et al.  Generalized Memristive Device SPICE Model and its Application in Circuit Design , 2013, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[2]  J. Yang,et al.  Anatomy of a Nanoscale Conduction Channel Reveals the Mechanism of a High‐Performance Memristor , 2011, Advanced materials.

[3]  S. Chaudhary,et al.  Memristive Behavior in Thin Anodic Titania , 2010, IEEE Electron Device Letters.

[4]  Wei Yang Lu,et al.  Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.

[5]  Chris Yakopcic,et al.  Memristor SPICE Modeling , 2012 .

[6]  David Moore,et al.  Silver chalcogenide based memristor devices , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[7]  Chris Yakopcic,et al.  Memristor-based neuron circuit and method for applying learning algorithm in SPICE? , 2014 .

[8]  J. Yang,et al.  Memristive switching mechanism for metal/oxide/metal nanodevices. , 2008, Nature nanotechnology.

[9]  Jacques-Olivier Klein,et al.  Robust neural logic block (NLB) based on memristor crossbar array , 2011, 2011 IEEE/ACM International Symposium on Nanoscale Architectures.

[10]  Chris Yakopcic,et al.  Memristor SPICE model and crossbar simulation based on devices with nanosecond switching time , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[11]  W. Lu,et al.  High-density Crossbar Arrays Based on a Si Memristive System , 2008 .

[12]  Chris Yakopcic,et al.  Efficacy of memristive crossbars for neuromorphic processors , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[13]  G. Subramanyam,et al.  A Memristor Device Model , 2011, IEEE Electron Device Letters.

[14]  Bernabé Linares-Barranco,et al.  On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex , 2011, Front. Neurosci..

[15]  L. Chua Memristor-The missing circuit element , 1971 .