With the end of Dennard scaling and the ever-increasing need for more efficient, faster computation, resistive switching devices (ReRAM), often referred to as memristors, are a promising candidate for next generation computer hardware. These devices show particular promise for use in an analog neuromorphic computing accelerator as they can be tuned to multiple states and be updated like the weights in neuromorphic algorithms. Modeling a ReRAM-based neuromorphic computing accelerator requires a compact model capable of correctly simulating the small weight update behavior associated with neuromorphic training. These small updates have a nonlinear dependence on the initial state, which has a significant impact on neural network training. Consequently, we propose the piecewise empirical model (PEM), an empirically derived general purpose compact model that can accurately capture the nonlinearity of an arbitrary two-terminal device to match pulse measurements important for neuromorphic computing applications. By defining the state of the device to be proportional to its current, the model parameters can be extracted from a series of voltages pulses that mimic the behavior of a device in an analog neuromorphic computing accelerator. This allows for a general, accurate, and intuitive compact circuit model that is applicable to different resistance-switching device technologies. In this work, we explain the details of the model, implement the model in the circuit simulator Xyce, and give an example of its usage to model a specific Ta / TaO x device.
Resistive memory (ReRAM) shows promise for use as an analog synapse element in energy-efficient neural network algorithm accelerators. A particularly important application is the training of neural networks, as this is the most computationally-intensive procedure in using a neural algorithm. However, training a network with analog ReRAM synapses can significantly reduce the accuracy at the algorithm level. In order to assess this degradation, analog properties of ReRAM devices were measured and hand-written digit recognition accuracy was modeled for the training using backpropagation. Bipolar filamentary devices utilizing three material systems were measured and compared: one oxygen vacancy system, Ta-TaOx, and two conducting metallization systems, Cu-SiO2, and Ag/chalcogenide. Analog properties and conductance ranges of the devices are optimized by measuring the response to varying voltage pulse characteristics. Key analog device properties which degrade the accuracy are update linearity and write noise. Write noise may improve as a function of device manufacturing maturity, but write nonlinearity appears relatively consistent among the different device material systems and is found to be the most significant factor affecting accuracy. This suggests that new materials and/or fundamentally different resistive switching mechanisms may be required to improve device linearity and achieve higher algorithm training accuracy.
Analog resistive memories promise to reduce the energy of neural networks by orders of magnitude. However, the write variability and write nonlinearity of current devices prevent neural networks from training to high accuracy. We present a novel periodic carry method that uses a positional number system to overcome this while maintaining the benefit of parallel analog matrix operations. We demonstrate how noisy, nonlinear TaOx devices that could only train to 80% accuracy on MNIST, can now reach 97% accuracy, only 1% away from an ideal numeric accuracy of 98%. On a file type dataset, the TaOx devices achieve ideal numeric accuracy. In addition, low noise, linear Li1-xCoO2 devices train to ideal numeric accuracies using periodic carry on both datasets.
Analog resistive memories promise to reduce the energy of neural networks by orders of magnitude. However, the write variability and write nonlinearity of current devices prevent neural networks from training to high accuracy. We present a novel periodic carry method that uses a positional number system to overcome this while maintaining the benefit of parallel analog matrix operations. We demonstrate how noisy, nonlinear TaOx devices that could only train to 80% accuracy on MNIST, can now reach 97% accuracy, only 1% away from an ideal numeric accuracy of 98%. On a file type dataset, the TaOx devices achieve ideal numeric accuracy. In addition, low noise, linear Li1-xCoO2 devices train to ideal numeric accuracies using periodic carry on both datasets.
Resistive memories enable dramatic energy reductions for neural algorithms. We propose a general purpose neural architecture that can accelerate many different algorithms and determine the device properties that will be needed to run backpropagation on the neural architecture. To maintain high accuracy, the read noise standard deviation should be less than 5% of the weight range. The write noise standard deviation should be less than 0.4% of the weight range and up to 300% of a characteristic update (for the datasets tested). Asymmetric nonlinearities in the change in conductance vs pulse cause weight decay and significantly reduce the accuracy, while moderate symmetric nonlinearities do not have an effect. In order to allow for parallel reads and writes the write current should be less than 100 nA as well.
Resistive memories enable dramatic energy reductions for neural algorithms. We propose a general purpose neural architecture that can accelerate many different algorithms and determine the device properties that will be needed to run backpropagation on the neural architecture. To maintain high accuracy, the read noise standard deviation should be less than 5% of the weight range. The write noise standard deviation should be less than 0.4% of the weight range and up to 300% of a characteristic update (for the datasets tested). Asymmetric nonlinearities in the change in conductance vs pulse cause weight decay and significantly reduce the accuracy, while moderate symmetric nonlinearities do not have an effect. In order to allow for parallel reads and writes the write current should be less than 100 nA as well.
We present a study of the 'snap-back' regime of resistive switching hysteresis in bipolar TaOx memristors, identifying power signatures in the electronic transport. Using a simple model based on the thermal and electric field acceleration of ionic mobilities, we provide evidence that the 'snap-back' transition represents a crossover from a coupled thermal and electric-field regime to a primarily thermal regime, and is dictated by the reconnection of a ruptured conducting filament. We discuss how these power signatures can be used to limit filament radius growth, which is important for operational properties such as power, speed, and retention.
The locations of conductive regions in TaOx memristors are spatially mapped using a microbeam and Nanoimplanter by rastering an ion beam across each device while monitoring its resistance. Microbeam irradiation with 800 keV Si ions revealed multiple sensitive regions along the edges of the bottom electrode. The rest of the active device area was found to be insensitive to the ion beam. Nanoimplanter irradiation with 200 keV Si ions demonstrated the ability to more accurately map the size of a sensitive area with a beam spot size of 40 nm by 40 nm. Isolated single spot sensitive regions and a larger sensitive region that extends approximately 300 nm were observed.
The locations of conductive regions in TaOx memristors are spatially mapped using a microbeam and Nanoimplanter by rastering an ion beam across each device while monitoring its resistance. Microbeam irradiation with 800 keV Si ions revealed multiple sensitive regions along the edges of the bottom electrode. The rest of the active device area was found to be insensitive to the ion beam. Nanoimplanter irradiation with 200 keV Si ions demonstrated the ability to more accurately map the size of a sensitive area with a beam spot size of 40 nm by 40 nm. Isolated single spot sensitive regions and a larger sensitive region that extends approximately 300 nm were observed.
This paper investigates the effects of high dose rate ionizing radiation and total ionizing dose (TID) on tantalum oxide (TaOx) memristors. Transient data were obtained during the pulsed exposures for dose rates ranging from approximately 5.0 ×107 rad(Si)/s to 4.7 ×108 rad(Si)/s and for pulse widths ranging from 50 ns to 50 μs. The cumulative dose in these tests did not appear to impact the observed dose rate response. Static dose rate upset tests were also performed at a dose rate of ~3.0 ×108 rad(Si)/s. This is the first dose rate study on any type of memristive memory technology. In addition to assessing the tolerance of TaOx memristors to high dose rate ionizing radiation, we also evaluated their susceptibility to TID. The data indicate that it is possible for the devices to switch from a high resistance off-state to a low resistance on-state in both dose rate and TID environments. The observed radiation-induced switching is dependent on the irradiation conditions and bias configuration. Furthermore, the dose rate or ionizing dose level at which a device switches resistance states varies from device to device; the enhanced susceptibility observed in some devices is still under investigation. As a result, numerical simulations are used to qualitatively capture the observed transient radiation response and provide insight into the physics of the induced current/voltages.