Memristive devices for neuromorphic computation Lus Guerra IFIMUPIN
![The Memristor Theorized in 1971[1], physically achieved in 2008[2]: - Two-terminal passive circuit element;](https://slidetodoc.com/presentation_image_h/77c5bb3fa2d93b1041c7cb6897a2d454/image-3.jpg "The Memristor Theorized in 1971[1], physically achieved in 2008[2]: - Two-terminal passive circuit element;")
- Slides: 11
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 1 st International Baku Forum 23 rd of May, 2013
Outline • • The Memristor Applications Neuromorphic Computation Fabrication Results Willshaw Network Conclusions
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. 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 Theory CT-18, 507– 519 (1971).
Applications Resistive Random Access Memories (Re. RAM) - Non-volatile, reversible resistive switching; - High-speed and high ON/OFF ratio; - High-density; - Possibly multi-level; HP Toshiba Sandisk Samsung Panasonic 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” From: Mead, C. Neuromorphic electronic systems. Proceedings of the IEEE 78, 1629– 1636 (1990). - Uncanny resemblance to biological synapses.
Neuromorphic Computation Even the simplest brain is superior to a super computer, the secret: ARCHITECTURE! Human brain: - 106 neurons / cm 2 - 1010 synapses / cm 2 - 2 m. W / cm 2 Total power consumption: 20 Watts Memristors: - Cheap - Power efficient - Small From: Versace, M. & Chandler, B. The brain of a new machine. Spectrum, IEEE (2010).
Fabrication Two-terminal resistance switches, typically a thin-film metalinsulator-metal (MIM) stack: Metals: Device area: Ag, Al, Cu, 1 – 100 μm 2 - Ion-beam for film deposition; Pt, Ru, Ti. - Optical litography for microfrabrication. Insulator: Hf. O 2 150 μm 2 From: Strukov, D. B. & Kohlstedt, H. Resistive switching phenomena in thin films: Materials, devices, and applications. MRS Bulletin 37, 108– 114 (2012).
Results Device area: 9 μm 2 - Bipolar switching; SET (HRS to LRS) and RESET (LRS to HRS) processes; SET current compliance; Loss of hysteresis with consecutive loops.
Results Device area: 1 μm 2 - Bipolar switching; - SET current compliance; - High reset current / high Vset variability;
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.
Conclusions Memristor open possibilities for applications in: - Re. RAM 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.
Acknowledgments: J. Ventura, C. Dias, P. Aguiar, J. Pereira, S. Freitas, P. P. Freitas Thank you for your attention
