memristive devices for neuromorphic computation
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
Memristive devices for neuromorphic computation
Luís GuerraIFIMUP-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
• The Memristor• Applications• Neuromorphic Computation• Fabrication• Results• Willshaw Network• Conclusions
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.
The Memristor
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)
𝜔1≫𝜔2≫𝜔3
[1] Chua, L. Memristor - The Missing Circuit Element. IEEE Transactions On Circuit Theory CT-18, 507–519 (1971).
ApplicationsResistive 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.
HPToshibaSandisk
SamsungPanasonic
From: Mead, C. Neuromorphic electronic systems. Proceedings of the IEEE 78, 1629–1636 (1990).
Neuromorphic ComputationEven the simplest brain is superior to a super computer,the secret: ARCHITECTURE!
From: Versace, M. & Chandler, B. The brain of a new machine. Spectrum, IEEE (2010).
Human brain:- 106 neurons / cm2
- 1010 synapses / cm2
- 2 mW / cm2
Total power consumption: 20 Watts
Memristors:- Cheap- Power efficient- Small
FabricationTwo-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).
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- 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
Results
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Inset showing SETs in detail
0 2 4 6 8 10 12 14 16 18 20 221E-91E-81E-71E-61E-51E-41E-30.010.1
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- Bipolar switching;- SET current compliance;- High reset current / high Vset variability;
Device area: 1 μm2
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.
ConclusionsMemristor 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.
Thank you for your attention
Acknowledgments:J. Ventura, C. Dias, P. Aguiar, J. Pereira, S. Freitas, P. P. Freitas