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Characterization and Modeling of the Conduction and Switching Mechanisms of HfOx Based RRAM

Published online by Cambridge University Press:  10 February 2014

Shimeng Yu  and
H.-S. Philip Wong
Shimeng Yu
Affiliation:
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA
H.-S. Philip Wong
Affiliation:
Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
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Abstract

The conduction and switching mechanism of resistive random access memory (RRAM) is reviewed in this paper. The resistive switching in oxides is generally attributed to the conductive filament (made up of oxygen vacancies) formation and rupture in the oxide due to field assisted oxygen ion migration. As a model system for device physics study, HfOx based RRAM devices were fabricated and characterized. To identify the electron conduction mechanism, various electrical characterization techniques such as I-V measurements at various temperatures, low-frequency noise measurements, and AC conductance measurements were employed. It was suggested that the trap-assisted-tunneling is the dominant conduction mechanism. In order to explore the oxygen ion migration dynamics, pulse switching measurements were performed. An exponential voltage-time relationship was found between the switching time and the applied voltage. To obtain a first-order understanding of the variability of resistive switching, a Kinetic Monte Carlo (KMC) numerical simulator was developed. The generation/recombination/migration probabilities of oxygen vacancies and oxygen ions were calculated, and the conductive filament configuration was updated stochastically according to those probabilities. The KMC simulation can reproduce many experimental observations in the DC I-V sweep, pulse switching, endurance cycling, and retention baking, etc. The tail bits in the resistance distribution are attributed to the oxygen vacancy left over in the gap region due to a competition between the oxygen vacancy generation and recombination. To enable circuit and system development using RRAM, a compact device model was developed. The compact model, implemented in MATLAB, HSPICE, and Verilog-A, which can be employed in many commonly available circuit simulators using the SPICE engine.

Keywords

dielectric propertieselectrical propertiesmetal-insulator transition
Type
Articles
Information
MRS Online Proceedings Library (OPL) , Volume 1631: Symposium P – Emergent Electron Transport Properties at Complex Oxide Interfaces , 2014 , mrsf13-1631-p04-03
Copyright
Copyright © Materials Research Society 2014 

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References

REFERENCES

Wong, H.-S. P., Lee, H.-Y., Yu, S., Chen, Y.-S., Wu, Y., Chen, P.-S., Lee, B., Chen, F. T., and Tsai, M.-J., “Metal–oxide RRAM,” Proceedings of the IEEE, vol. 100, no. 6, pp. 1951–1970, 2012.10.1109/JPROC.2012.2190369CrossRefGoogle Scholar
Waser, R., et al. ., “Redox-based resistive switching memories - nanoionic mechanisms, prospects, and challenges,” Advanced Materials, vol. 21, no. 25–26, pp. 2632–2663, 2009.10.1002/adma.200900375CrossRefGoogle Scholar
Bersuker, G., et al. ., “Metal oxide RRAM switching mechanism based on conductive filament microscopic properties,” IEEE International Electron Devices Meeting, pp. 456–459, 2010.Google Scholar
Yu, S., and Wong, H.-S. P., “A phenomenological model for the reset mechanism of metal oxide RRAM,” IEEE Electron Device Letters, vol. 31, no. 12, pp. 1455–1457, 2010.10.1109/LED.2010.2078794CrossRefGoogle Scholar
Lee, H. Y., et al. ., “Low power and high speed bipolar switching with a thin reactive Ti buffer layer in robust HfO2 based RRAM,” IEEE International Electron Devices Meeting, pp. 297–300, 2008.Google Scholar
Sheu, S.-S., et al. ., “A 4Mb embedded SLC resistive-RAM macro with 7.2ns read-write random-access time and 160ns MLC-access capability,” IEEE International Solid-State Circuits Conference, pp. 200–202, 2011.Google Scholar
Govoreanu, B., et al. ., “10×10nm2 Hf /HfOx crossbar resistive RAM with excellent performance, reliability and low-energy operation,” IEEE International Electron Devices Meeting, pp. 729–732, 2011.Google Scholar
Yu, S., Wu, Y., Chai, Y., Provine, J. and Wong, H.-S. P., “Characterization of switching parameters and multilevel capability in HfOx/AlOx bi-layer RRAM devices”, International Symposium on VLSI Technology, Systems and Applications, pp. 106–107, 2011.Google Scholar
Yu, S., Guan, X., and Wong, H.-S. P., “Conduction mechanism of TiN/HfOx/Pt resistive switching memory: a trap-assisted-tunneling model”, Appl. Phys. Lett., vol. 99, 063507, 2011.10.1063/1.3624472CrossRefGoogle Scholar
Yu, S., Jeyasingh, R., Wu, Y., and Wong, H.-S. P., “Characterization of low-frequency noise in the resistive switching of transition metal oxide HfO2 ”, Phys. Rev. B, vol. 85, 045324, 2012.10.1103/PhysRevB.85.045324CrossRefGoogle Scholar
Yu, S., Jeyasingh, R., Wu, Y., and Wong, H.-S. P., “AC conductance measurement and analysis of the conduction processes in HfOx based resistive switching memory”, Appl. Phys. Lett., vol. 99, 232105, 2011.10.1063/1.3663968CrossRefGoogle Scholar
Jonscher, A. K., “Dielectric relaxation in solids,” Journal of Physics D: Applied Physics, vol. 32, p. R57, 1999.10.1088/0022-3727/32/14/201CrossRefGoogle Scholar
Yu, S., Wu, Y., and Wong, H.-S. P., “Investigating the switching dynamics and multilevel capability of bipolar metal oxide resistive switching memory”, Appl. Phys. Lett., vol. 98, 103514, 2011.10.1063/1.3564883CrossRefGoogle Scholar
Yu, S., Guan, X., and Wong, H.-S. P., “On the stochastic nature of resistive switching in metal oxide RRAM: physical modeling, Monte Carlo simulation, and experimental characterization”, IEEE International Electron Devices Meeting, pp. 413–416, 2011.Google Scholar
Yu, S., Guan, X., and Wong, H.-S. P., “Understanding metal oxide RRAM current overshoot and reliability using Kinetic Monte Carlo simulation”, IEEE International Electron Devices Meeting, pp. 585–588, 2012.Google Scholar
Yu, S., Gao, B., Fang, Z., Yu, H. Y., Kang, J. F., and 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”, IEEE International Electron Devices Meeting, pp. 239–242, 2012.Google Scholar
Guan, X., Yu, S., and Wong, H.-S. P., “A SPICE compact model of metal oxide resistive switching memory with variations”, IEEE Electron Device Lett., vol. 33, no. 10, pp. 1405–1407, 2012.10.1109/LED.2012.2210856CrossRefGoogle Scholar