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).
𝜔 1 ≫ 𝜔 2 ≫ 𝜔 3
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 Theory CT-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 IEEE 78, 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 Bulletin 37, 108–114 (2012).

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

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

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