Adaptive oxide electronics: A review

被引:234
作者
Ha, Sieu D. [1 ]
Ramanathan, Shriram [1 ]
机构
[1] Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA
关键词
ARTIFICIAL NEURAL-NETWORKS; CONTENT-ADDRESSABLE MEMORY; OPTICAL-PROPERTIES; BAND-GAP; FERROELECTRIC MEMORY; TUNNEL-JUNCTIONS; RESET MECHANISM; SPIKING NEURONS; THIN-FILMS; MEMRISTOR;
D O I
10.1063/1.3640806
中图分类号
O59 [应用物理学];
学科分类号
摘要
Novel information processing techniques are being actively explored to overcome fundamental limitations associated with CMOS scaling. A new paradigm of adaptive electronic devices is emerging that may reshape the frontiers of electronics and enable new modalities. Creating systems that can learn and adapt to various inputs has generally been a complex algorithm problem in information science, albeit with wide-ranging and powerful applications from medical diagnosis to control systems. Recent work in oxide electronics suggests that it may be plausible to implement such systems at the device level, thereby drastically increasing computational density and power efficiency and expanding the potential for electronics beyond Boolean computation. Intriguing possibilities of adaptive electronics include fabrication of devices that mimic human brain functionality: the strengthening and weakening of synapses emulated by electrically, magnetically, thermally, or optically tunable properties of materials. In this review, we detail materials and device physics studies on functional metal oxides that may be utilized for adaptive electronics. It has been shown that properties, such as resistivity, polarization, and magnetization, of many oxides can be modified electrically in a non-volatile manner, suggesting that these materials respond to electrical stimulus similarly as a neural synapse. We discuss what device characteristics will likely be relevant for integration into adaptive platforms and then survey a variety of oxides with respect to these properties, such as, but not limited to, TaOx, SrTiO3, and Bi4-xLaxTi3O12. The physical mechanisms in each case are detailed and analyzed within the framework of adaptive electronics. We then review theoretically formulated and current experimentally realized adaptive devices with functional oxides, such as self-programmable logic and neuromorphic circuits. Finally, we speculate on what advances in materials physics and engineering may be needed to realize the full potential of adaptive oxide electronics. (C) 2011 American Institute of Physics. [doi:10.1063/1.3640806]
引用
收藏
页数:20
相关论文
共 217 条
[1]   Synaptic plasticity: taming the beast [J].
Abbott, L. F. ;
Nelson, Sacha B. .
NATURE NEUROSCIENCE, 2000, 3 (11) :1178-1183
[2]   Electronic structure of silicon interfaces with amorphous and epitaxial insulating oxides:: Sc2O3, Lu2O3, LaLuO3 [J].
Afanas'ev, V. V. ;
Shamuilia, S. ;
Badylevich, M. ;
Stesmans, A. ;
Edge, L. F. ;
Tian, W. ;
Schlom, D. G. ;
Lopes, J. M. J. ;
Roeckerath, M. ;
Schubert, J. .
MICROELECTRONIC ENGINEERING, 2007, 84 (9-10) :2278-2281
[3]   Resistive Random Access Memory (ReRAM) Based on Metal Oxides [J].
Akinaga, Hiroyuki ;
Shima, Hisashi .
PROCEEDINGS OF THE IEEE, 2010, 98 (12) :2237-2251
[4]   An Adaptive System for Optimal Solar Energy Harvesting in Wireless Sensor Network Nodes [J].
Alippi, Cesare ;
Galperti, Cristian .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2008, 55 (06) :1742-1750
[5]  
Almasi G, 2008, IBM J RES DEV, V52, P199
[6]  
[Anonymous], 2009, P C HIGH PERFORMANCE
[7]   Relationship between resistive switching characteristics and band diagrams of Ti/Pr1-xCaxMnO3 junctions [J].
Asanuma, S. ;
Akoh, H. ;
Yamada, H. ;
Sawa, A. .
PHYSICAL REVIEW B, 2009, 80 (23)
[8]   Bankruptcy prediction for credit risk using neural networks: A survey and new results [J].
Atiya, AF .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2001, 12 (04) :929-935
[9]   Highly scalable non-volatile resistive memory using simple binary oxide driven by asymmetric unipolar voltage pulses [J].
Baek, IG ;
Lee, MS ;
Seo, S ;
Lee, MJ ;
Seo, DH ;
Suh, DS ;
Park, JC ;
Park, SO ;
Kim, HS ;
Yoo, IK ;
Chung, UI ;
Moon, JT .
IEEE INTERNATIONAL ELECTRON DEVICES MEETING 2004, TECHNICAL DIGEST, 2004, :587-590
[10]   Using neural network rule extraction and decision tables for credit-risk evaluation [J].
Baesens, B ;
Setiono, R ;
Mues, C ;
Vanthienen, J .
MANAGEMENT SCIENCE, 2003, 49 (03) :312-329