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Memristive devices for neuromorphic computation

Memristive devices for neuromorphic computation. Luís Guerra IFIMUP-IN (Material Physics Institute of the University of Porto – Nanoscience and Nanotechnology Institute). New Challenges in the European Area: Young Scientist’s 1st International Baku Forum 23rd of May, 2013. Outline.

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Memristive devices for neuromorphic computation

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  1. Memristive devices for neuromorphic computation Luís Guerra IFIMUP-IN (Material Physics Institute of the University of Porto – Nanoscience and Nanotechnology Institute) New Challenges in the European Area: Young Scientist’s 1st International Baku Forum 23rd of May, 2013

  2. Outline • The Memristor • Applications • Neuromorphic Computation • Fabrication • Results • Willshaw Network • Conclusions

  3. The Memristor Theorized in 1971[1], physically achieved in 2008[2]: • Two-terminal passive circuit element; • Resistance depends on the history of applied voltage or current; • Self-crossing, pinched hysteretic I-V loop, frequency dependent. From [2]: D. B. Strukov, G. S. Snider, D. R. Stewart, and R. S. Williams, Nature 453, 80 (2008). From: Y. V. Pershin and M. Di Ventra, Advances in Physics 60, 145–227 (2011) [1] Chua, L. Memristor - The Missing Circuit Element. IEEE Transactions On Circuit TheoryCT-18, 507–519 (1971).

  4. Applications Resistive Random Access Memories (ReRAM) • Non-volatile, reversible resistive switching; • High-speed and high ON/OFF ratio; • High-density; • Possibly multi-level; Neuromorphic computation – “the use of very-large-scale integration (VLSI) systems, containing electronic analog circuits, to mimic neuro-biological architectures present in the nervous system” - Uncanny resemblance to biological synapses. HP Toshiba Sandisk Samsung Panasonic From: Mead, C. Neuromorphic electronic systems. Proceedings of the IEEE78, 1629–1636 (1990).

  5. Neuromorphic Computation Even the simplest brain is superior to a super computer, the secret: ARCHITECTURE! • Human brain: • 106 neurons / cm2 • 1010 synapses / cm2 • 2 mW / cm2 • Total power consumption: 20 Watts • Memristors: • Cheap • Power efficient • Small From: Versace, M. & Chandler, B. The brain of a new machine. Spectrum, IEEE (2010).

  6. Fabrication Two-terminal resistance switches, typically a thin-film metal-insulator-metal (MIM) stack: • Ion-beam for film deposition; • Optical litography for microfrabrication. Metals: Ag, Al, Cu, Pt, Ru, Ti. Insulator: HfO2 Device area: 1 – 100 μm2 150 μm2 From: Strukov, D. B. & Kohlstedt, H. Resistive switching phenomena in thin films: Materials, devices, and applications. MRS Bulletin37, 108–114 (2012).

  7. Results • Bipolar switching; • SET (HRS to LRS) and RESET (LRS to HRS) processes; • SET current compliance; • Loss of hysteresis with consecutive loops. Device area: 9 μm2

  8. Results • Bipolar switching; • SET current compliance; • High reset current / high Vset variability; Device area: 1 μm2

  9. Willshaw Network Associative memory mapping an input vector into an output vector via a matrix of binary synapses (memristors); Nanodevices have high defect rates Work around them! Study of Stuck-at-0 (OFF) and Stuck-at-1 (ON) defects. Capacity and robustness to noise can be improved by adjusting the current readout threshold, according to the type of predominant defect.

  10. Conclusions Memristor open possibilities for applications in: • ReRAM and Neuromorphic computation, among others. Key features of memristors: • Resemblance to biological synapses; • High scalability, below 10 nm; • CMOS compatible; • Fast, non-volatile, electrical switching; • Low power consumption; • Cheap.

  11. Acknowledgments: J. Ventura, C. Dias, P. Aguiar, J. Pereira, S. Freitas, P. P. Freitas Thank you for your attention

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