Ting Chang

Nanoscale Memristor Device as Synapse in Neuromorphic Systems

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.

Observation of conducting filament growth in nanoscale resistive memories

Nanoscale resistive switching devices, sometimes termed memristors, have recently generated significant interest for memory, logic and neuromorphic applications. Resistive switching effects in dielectric-based devices are normally assumed to be caused by conducting filament formation across the electrodes, but the nature of the filaments and their growth dynamics remain controversial. Here we report direct transmission electron microscopy imaging, and structural and compositional analysis of the nanoscale conducting filaments. Through systematic ex-situ and in-situ transmission electron microscopy studies on devices under different programming conditions, we found that the filament growth can be dominated by cation transport in the dielectric film. Unexpectedly, two different growth modes were observed for the first time in materials with different microstructures. Regardless of the growth direction, the narrowest region of the filament was found to be near the dielectric/inert-electrode interface in these devices, suggesting that this region deserves particular attention for continued device optimization.

Si Memristive devices applied to memory and neuromorphic circuits

We report studies on nanoscale Si-based memristive devices for memory and neuromorphic applications. The devices are based on ion motion inside an insulating a-Si matrix. Digital devices show excellent performance metrics including scalability, speed, ON/OFF ratio, endurance and retention. High density non-volatile memory arrays based on a crossbar structure have been fabricated and tested. Devices inside a 1kb array can be individually addressed with excellent reproducibility and reliability. By adjusting the device and material structures, nanoscale analog memristor devices have also been demonstrated. The analog memristor devices exhibit incremental conductance changes that are controlled by the charge flown through the device. The performances of the digital and analog devices are thought to be determined by the formation of a dominant conducting filament and the continuous motion of a uniform conduction front, respectively.

Two-terminal resistive switches (memristors) for memory and logic applications

We review the recent progress on the development of two-terminal resistive devices (memristors). Devices based on solid-state electrolytes (e.g. a-Si) have been shown to possess a number of promising performance metrics such as yield, on/off ratio, switching speed, endurance and retention suitable for memory or reconfigurable circuit applications. In addition, devices with incremental resistance changes have been demonstrated and can be used to emulate synaptic functions in hardware based neuromorphic circuits. Device and SPICE modeling based on a properly chosen internal state variable have been carried out and will be useful for large-scale circuit simulations.

Device and SPICE modeling of RRAM devices

We report the development of physics based models for resistive random-access memory (RRAM) devices. The models are based on a generalized memristive system framework and can explain the dynamic resistive switching phenomena observed in a broad range of devices. Furthermore, by constructing a simple subcircuit, we can incorporate the device models into standard circuit simulators such as SPICE. The SPICE models can accurately capture the dynamic effects of the RRAM devices such as the apparent threshold effect, the voltage dependence of the switching time, and multi-level effects under complex circuit conditions. The device and SPICE models can also be readily expanded to include additional effects related to internal state changes, and will be valuable to help in the design and simulation of memory and logic circuits based on resistive switching devices.

Building Neuromorphic Circuits with Memristive Devices

The rapid, exponential growth of modern electronics has brought about profound changes to our daily lives. However, maintaining the growth trend now faces significant challenges at both the fundamental and practical levels [1]. Possible solutions include More Moore?developing new, alternative device structures and materials while maintaining the same basic computer architecture, and More Than Moore?enabling alternative computing architectures and hybrid integration to achieve increased system functionality without trying to push the devices beyond limits. In particular, an increasing number of computing tasks today are related to handling large amounts of data, e.g. image processing as an example. Conventional von Neumann digital computers, with separate memory and processer units, become less and less efficient when large amount of data have to be moved around and processed quickly. Alternative approaches such as bio-inspired neuromorphic circuits, with distributed computing and localized storage in networks, become attractive options [2]?[6].

Interference and memory capacity effects in memristive systems

Short-term memory implies the existence of a capacity limit beyond which memory cannot be securely formed and retained. The underlying mechanisms are believed to be two primary factors: decay and interference. Here, we demonstrate through both simulation and experiment that the memory capacity effect can be implemented in a parallel memristor circuit, where decay and interference are achieved by the inherent ion diffusion in the device and the competition for current supply in the circuit, respectively. This study suggests it is possible to emulate high-level biological behaviors with memristor circuits and will stimulate continued studies on memristor-based neuromorphic circuits.

Modeling and implementation of oxide memristors for neuromorphic applications

We report the fabrication, modeling and implementation of nanoscale tungsten-oxide (WOx) memristive (memristor) devices for neuromorphic applications. The device behaviors can be predicted accurately by considering both ion drift and diffusion. Short-term memory and memory enhancement phenomena, and the effects of spike rate, timing and associativity have been demonstrated. SPICE modeling has been achieved that allows circuit-level implementations.

Memristive Devices: Switching Effects, Modeling, and Applications

The rapid, exponential growth of modern electronics has brought about profound changes to our daily lives. However, maintaining the growth trend now faces significant challenges at both the fundamental and practical levels [1]. Possible solutions include More Moore—developing new, alternative device structures, and materials while maintaining the same basic computer architecture, and More Than Moore—enabling alternative computing architectures and hybrid integration to achieve increased system functionality without trying to push the devices beyond limits. In particular, an increasing number of computing tasks today are related to handling large amounts of data, e.g. image processing as an example. Conventional von Neumann digital computers, with separate memory and processer units, become less and less efficient when large amount of data have to be moved around and processed quickly. Alternative approaches such as bio-inspired neuromorphic circuits, with distributed computing and localized storage in networks, become attractive options [2–6].