Data clustering using memristor networks

Scientific Reports

Volumn 5, Issue , 2015, Pages

  Choi, Shinhyun ^a   Sheridan, Patrick ^a   Lu, Wei D ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84930646705     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep10492     Document Type: Article

References (24)

1. Turel, Ö., Leem, J. H., Ma, X. & Likharev, K. K. Neuromorphic architectures for nanoelectronic circuits, Inter. J. Circuit Theory and Applications, 32, 277-302 (2004).
2. Chua, L. O. & Yang, L. Cellular neural networks: Theory. IEEE Trans. Circuits and Systems-I, 35, 1257-72 (1988).
3. Jolliffe, I. T. Principal Component Analysis. (Springer, New York, 2002).
4. Alibart, F., Zamanidoost, E. & Strukov, D. B. Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat. Commun. 4, 2072 (2013).
5. Sheridan, P., Ma, W. & Lu, W. Pattern recognition with memristor networks. IEEE Inter. Symp. Circuits and Systems (ISCAS), 1078-81 (2014).
6. Lee, M.-J. et al. A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5-x/TaO2-x bilayer structures. Nat. Mater., 10, 625-30 (2011).
7. Yang, J.-J. et al. High switching endurance in TaOx memristive devices. Appl. Phys. Lett., 97, 232102 (2010).
8. Torrezan, A. C., Strachan, J. P., Medeiros-Ribeiro, G. & Williams, R. S. Sub-nanosecond switching of a tantalum oxide memristor. Nanotechnology, 22, 485203 (2011).
9. Govoreanu, B. et al. 10 × 10nm2 Hf/HfOx crossbar resistive RAM with excellent performance, reliability and low-energy operation. Electron Devices Meeting (IEDM), 2011 IEEE Inter., 31-36 (2011).
10. Yang, Y., Sheridan, P. & Lu, W. Complementary resistive switching in tantalum oxide-based resistive memory devices. Appl. Phys. Lett., 100, 203112 (2012).
11. Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80-84 (2008).
12. Yu, S., Guan, X. & Wong, H.-S. P. Conduction mechanism of TiN?HfOx?Pt resistive switching memory: A trap-assisted-tunneling model. Appl. Phys. Lett. 99, 063507 (2011).
13. Chang, T., Jo, S., Kim, K. H., Sheridan, P., Gaba, S. & Lu, W. Synaptic behaviors and modeling of a metal oxide memristive device. Appl. Phys. A, 102, 857-63 (2011).
14. Kim, S., Choi, S. & Lu, W. Comprehensive physical model of dynamic resistive switching in an oxide memristor. ACS Nano, 8, 2369-76 (2014).
15. Mickel, P. R., Lohn, A. J., James, C. D. & Marinella, M. J. Isothermal switching and detailed filament evolution in memristive systems. Adv. Mater. 26, 4486-90 (2014).
16. Choi, S., Lee, J., Kim, S. & Lu, W. D. Retention failure analysis of metal-oxide based resistive memory. Appl. Phys. Lett. 105, 113510 (2014).
17. Kim, S., Choi, S., Lee, J. & Lu, W. D. Tuning resistive switching characteristics of tantalum oxide memristors through Si doping. ACS Nano, 8, 10262-69 (2014).
18. Bache, K. & Lichman, M. Breast cancer Wisconsin (diagnostic) data set-UCI Machine Learning Repository (2013) Available at: Http://archive.ics.uci.edu/ml. (Accessed: 6th July 2014).
19. Wolberg, W. H. & Mangasarian, O. L. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Natl. Acad. Sci, 87, 9193-96 (1990).
20. Oja, E. Simplified neuron model as a principal component analyzer. J. Mathmatical, 15, 267-73 (1982).
21. Sanger, T. D. Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Networks, 2, 459-73 (1989).
22. Bishop, C. M. Pattern recognition and machine learning [205] (Springer, New York, 2006).
23. Walt. S. Van Der, Africa, S. & Feb, M. S. The NumPy array : A structure for efficient numerical computation. Computing in Science & Engineering, 13, 22-30 (2011).
24. Hunter, J. D. Matplotlib: A 2D graphics environment. Computing In Science & Engineering, 9, 90-95 (2007).