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ABSTRACT: "Memory" is an essential building block in learning and decision-making in biological systems. Unlike modern semiconductor memory devices, needless to say, human memory is by no means eternal. Yet, forgetfulness is not always a disadvantage since it releases memory storage for more important or more frequently accessed pieces of information and is thought to be necessary for individuals to adapt to new environments. Eventually, only memories that are of significance are transformed from short-term memory into long-term memory through repeated stimulation. In this study, we show experimentally that the retention loss in a nanoscale memristor device bears striking resemblance to memory loss in biological systems. By stimulating the memristor with repeated voltage pulses, we observe an effect analogous to memory transition in biological systems with much improved retention time accompanied by additional structural changes in the memristor. We verify that not only the shape or the total number of stimuli is influential, but also the time interval between stimulation pulses (i.e., the stimulation rate) plays a crucial role in determining the effectiveness of the transition. The memory enhancement and transition of the memristor device was explained from the microscopic picture of impurity redistribution and can be qualitatively described by the same equations governing biological memories.
ACS Nano 08/2011; 5(9):7669-76. · 10.77 Impact Factor
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ABSTRACT: A memristor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. Here we experimentally demonstrate a nanoscale silicon-based memristor device and show that a hybrid system composed of complementary metal-oxide semiconductor neurons and memristor synapses can support important synaptic functions such as spike timing dependent plasticity. Using memristors as synapses in neuromorphic circuits can potentially offer both high connectivity and high density required for efficient computing.
Nano Letters 03/2010; 10(4):1297-301. · 13.20 Impact Factor
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ABSTRACT: We report studies on nanoscale resistive memory devices that exhibit diodelike I-V characteristics at on-state with reverse bias current suppressed to below 10−13 A and rectifying ratio >106. The intrinsic diodelike characteristics are robust during device operation and can survive >108 write/erase programming cycles. The devices can be programmed at reduced programming voltages compared to earlier studies without the initial high-voltage forming process. Multibit storage capability was also reported. The intrinsic diode characteristics provide a possible solution to suppress crosstalk in high-density crossbar memory or logic arrays particularly for those based on bipolar resistive switches (memristors).
Applied Physics Letters 01/2010; 96(5):053106-053106-3. · 3.84 Impact Factor
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International Symposium on Circuits and Systems (ISCAS 2010), May 30 - June 2, 2010, Paris, France; 01/2010
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Applied Physics A. 01/2010; 102:857.
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ABSTRACT: We demonstrate large-scale (1 kb) high-density crossbar arrays using a Si-based memristive system. A two-terminal hysteretic resistive switch (memristive device) is formed at each crosspoint of the array and can be addressed with high yield and ON/OFF ratio. The crossbar array can be implemented as either a resistive random-access-memory (RRAM) or a write-once type memory depending on the device configuration. The demonstration of large-scale crossbar arrays with excellent reproducibility and reliability also facilitates further studies on hybrid nano/CMOS systems.
Nano Letters 03/2009; 9(2):870-4. · 13.20 Impact Factor
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ABSTRACT: We show that in nanoscale two-terminal resistive switches the resistance switching can be dominated by the formation of a single conductive filament. The probabilistic filament formation process strongly affects the device operation principle, and can be programmed to facilitate new functionalities such as multibit switching with partially formed filaments. In addition, the nanoscale switches exhibit excellent performance metrics making them well suited for memory or logic operations using conventional or emerging hybrid nano/CMOS architectures.
Nano Letters 01/2009; 9(1):496-500. · 13.20 Impact Factor
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ABSTRACT: We report studies on a nanoscale resistance switching memory structure based on planar silicon that is fully compatible with CMOS technology in terms of both materials and processing techniques employed. These two-terminal resistance switching devices show excellent scaling potential well beyond 10 Gb/cm2 and exhibit high yield (99%), fast programming speed (5 ns), high on/off ratio (10(3)), long endurance (10(6)), retention time (5 months), and multibit capability. These key performance metrics compare favorably with other emerging nonvolatile memory techniques. Furthermore, both diode-like (rectifying) and resistor-like (nonrectifying) behaviors can be obtained in the device switching characteristics in a controlled fashion. These results suggest that the CMOS compatible, nanoscale Si-based resistance switching devices may be well suited for ultrahigh-density memory applications.
Nano Letters 03/2008; 8(2):392-7. · 13.20 Impact Factor
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ABSTRACT: Nanoscale Ag/a-Si:H/c-Si based resistive switching nonvolatile memory devices have been fabricated and examined with active areas down to 50*50 nm<sup>2</sup>. Close to 100% device yield was achieved without high voltage forming. The on/off resistance ratio increases as the device size is reduced, while the on-state resistance is insensitive to the device size down the smallest scales. This nanoscale resistive switching structure offers the potential as ultra-high density crossbar non-volatile memory devices. In the on-state, a rectifying I-V behavior was observed, a property desirable for large scale integration.
Nanotechnology Materials and Devices Conference, 2006. NMDC 2006. IEEE; 11/2006
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Sung Hyun Jo
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ABSTRACT: As the building block of semiconductor electronics, field effect transistor (FET), approaches the sub 100 nm regime, a number of fundamental and practical issues start to emerge such as short channel effects that prevent the FET from operating properly and sub-threshold slope non-scaling that leads to increased power dissipation. In terms of nonvolatile memory, it is generally believed that transistor based Flash memory will approach the end of scaling within about a decade. As a result, novel, non-FET based devices and architectures will likely be needed to satisfy the growing demands for high performance memory and logic electronics applications. In this thesis, we present studies on nanoscale resistance switching devices (memristive devices). The device shows excellent resistance switching properties such as fast switching time (< 50 ns), high on/off ratio (> 10^6), good data retention (> 6 years) and programming endurance (> 10^5). The studies suggest that the nonvolatile resistance switching in a nanoscale a-Si resistive switch is caused by the formation of a single conductive filament within 10 nm range near the bottom electrode. New functionalities, such as multi-bit switching with partially formed filaments, can be obtained by controlling the resistance switching process through current programming. As digital memory devices, the devices are ideally suited in the crossbar architecture which offers ultra-high density and intrinsic defect tolerance capability. As an example, a high-density (2 Gbits/cm^2) 1kb crossbar memory was demonstrated with excellent uniformity, high yield (> 92%) and ON/OFF ratio (> 10^3), proving its promising aspects for memory and reconfigurable logic applications. Furthermore, we demonstrated that properly designed devices can exhibit controlled analog switching behavior and function as flux controlled memristor devices. The analog memristors can be used in biology-inspired neuromorphic circuits in which signal processing efficiency orders of magnitude higher than conventional digital computer systems can be reached. As a prototype illustration, we showed Spike Timing Dependent Plasticity (STDP), one of the key learning rules in biological system, can be realized by CMOS neurons and nanoscale memristor synapses.
