Isha Gupta

Real-time encoding and compression of neuronal spikes by metal-oxide memristors

Advanced brain-chip interfaces with numerous recording sites bear great potential for investigation of neuroprosthetic applications. The bottleneck towards achieving an efficient bio-electronic link is the real-time processing of neuronal signals, which imposes excessive requirements on bandwidth, energy and computation capacity. Here we present a unique concept where the intrinsic properties of memristive devices are exploited to compress information on neural spikes in real-time. We demonstrate that the inherent voltage thresholds of metal-oxide memristors can be used for discriminating recorded spiking events from background activity and without resorting to computationally heavy off-line processing. We prove that information on spike amplitude and frequency can be transduced and stored in single devices as non-volatile resistive state transitions. Finally, we show that a memristive device array allows for efficient data compression of signals recorded by a multi-electrode array, demonstrating the technologys potential for building scalable, yet energy-efficient on-node processors for brain-chip interfaces.

A Cell Classifier for RRAM Process Development

Devices that exhibit resistive switching are promising components for future nanoelectronics with applications ranging from emerging memory to neuromorphic computing and biosensors. In this brief, we present an algorithm for identifying switchable devices, i.e., devices that can be programmed in distinct resistive states and that change their state predictably and repeatedly in response to input stimuli. The method is based on extrapolating the statistical significance of difference in between two distinct resistive states as measured from devices subjected to standardized bias protocols. The test routine is applied on distinct elements of 32×32 resistive-random-access-memory (RRAM) crossbar arrays and yields a measure of device switchability in the form of a statistical significance p-value. Ranking devices by p-value shows that switchable devices are typically found in the bottom 10% and are therefore easily distinguishable from nonfunctional devices. Implementation of this algorithm dramatically cuts RRAM testing time by granting fast access to the best devices in each array, as well as yield metrics.

Spatially resolved TiOx phases in switched RRAM devices using soft X-ray spectromicroscopy

Reduction in metal-oxide thin films has been suggested as the key mechanism responsible for forming conductive phases within solid-state memory devices, enabling their resistive switching capacity. The quantitative spatial identification of such conductive regions is a daunting task, particularly for metal-oxides capable of exhibiting multiple phases as in the case of TiOx. Here, we spatially resolve and chemically characterize distinct TiOx phases in localized regions of a TiOx–based memristive device by combining full-field transmission X-ray microscopy with soft X-ray spectroscopic analysis that is performed on lamella samples. We particularly show that electrically pre-switched devices in low-resistive states comprise reduced disordered phases with O/Ti ratios around 1.37 that aggregate in a ~100 nm highly localized region electrically conducting the top and bottom electrodes of the devices. We have also identified crystalline rutile and orthorhombic-like TiO2 phases in the region adjacent to the main reduced area, suggesting that the temperature increases locally up to 1000 K, validating the role of Joule heating in resistive switching. Contrary to previous studies, our approach enables to simultaneously investigate morphological and chemical changes in a quantitative manner without incurring difficulties imposed by interpretation of electron diffraction patterns acquired via conventional electron microscopy techniques.

Improving Detection Accuracy of Memristor-Based Bio-Signal Sensing Platform.

Recently a novel neuronal activity sensor exploiting the intrinsic thresholded integrator capabilities of memristor devices has been proposed. Extracellular potentials captured by a standard bio-signal acquisition platform are fed into a memristive device which reacts to the input by changing its resistive state (RS) only when the signal ampitude exceeds a threshold. Thus, significant peaks in the neural signal can be stored as non-volatile changes in memristor resistive state whilst noise is effectively suppressed. However, as a memristor is subjected to increasing numbers of supra-threshold stimuli during practical operation, it accumulates (RS) changes and eventually saturates. This leads to severely reduced neural activity detection capabilities. In this work we explore different signal processing and memristor operating procedure strategies in order to improve the detection rate of significant neuronal activity events. We analyse the data obtained from a single-memristive device biased with a reference neural recording and observe that performance can be improved markedly by a) increasing the frequency at which the memristor is reset to an initial resistive state where it is known to be highly responsive, b) appropriately preconditioning the input waveform through application of gain and offset in order to optimally exploit the intrinsic device behaviour. All results are validated by benchmarking obtained spike detection performance against a state-of-the-art template matching system utilising computationally-heavy, multi-dimensional, principal component analysis.

Towards a memristor-based spike-sorting platform

We present a new approach for performing spike-sorting through a memristor-based, neural-signal processing platform. We have previously shown that the inherent threshold property of the memristor allows spike-detection through nonvolatile resistive state transition. Here, a test memristive device is subjected to a neural recording and the periodically recorded resistive state changes are mapped to the amplitude of the spiking events. It is found that the resistive state changes can be differentiated into clusters, where each cluster can be mapped to a range of spiking events in the input neural waveform, thus indicating the address of source neuron.

X-Ray spectromicroscopy investigation of soft and hard breakdown in RRAM devices

Resistive random access memory (RRAM) is considered an attractive candidate for next generation memory devices due to its competitive scalability, low-power operation and high switching speed. The technology however, still faces several challenges that overall prohibit its industrial translation, such as low yields, large switching variability and ultimately hard breakdown due to long-term operation or high-voltage biasing. The latter issue is of particular interest, because it ultimately leads to device failure. In this work, we have investigated the physicochemical changes that occur within RRAM devices as a consequence of soft and hard breakdown by combining full-field transmission x-ray microscopy with soft x-ray spectroscopic analysis performed on lamella samples. The high lateral resolution of this technique (down to 25 nm) allows the investigation of localized nanometric areas underneath permanent damage of the metal top electrode. Results show that devices after hard breakdown present discontinuity in the active layer, Pt inclusions and the formation of crystalline phases such as rutile, which indicates that the temperature increased locally up to 1000 K.

A TiO2 ReRAM parameter extraction method

In this work, we present a parameter extraction method for TiO2 memristive devices that applies on a resistive switching rate model which embodies only four parameters for each voltage biasing polarity. The simple form of the model functions allows the derivation of a predictive analytical resistive state response expression under constant bias voltage. By employing corresponding experimental testing on the devices, we fit such constant bias responses exhibited by physical memristor samples on this analytical expression. Next, we apply a simple algorithm that extracts the suitable model parameters that capture the switching rate behavior of the characterized device in its voltage range of operation.

Dataset for Real-time encoding and compression of neuronal spikes by metal-oxide memristor

Advanced brain-chip interfaces with numerous recording sites bear great potential for investigation of neuroprosthetic applications. The bottleneck towards achieving an efficient bio-electronic link is the real-time processing of neuronal signals, which imposes excessive requirements on bandwidth, energy and computation capacity. Here, we present a unique concept where the intrinsic properties of memristive devices are exploited to compress information on neural spikes in real time. We demonstrate that the inherent voltage thresholds of metal-oxide memristors can be used for discriminating recorded spiking events from background activity and without resorting to computationally heavy off-line processing. We prove that information on spike amplitude and frequency can be transduced and stored in single devices as non-volatile resistive state transitions. Finally, we show that a memristive device array allows for efficient data compression of signals recorded by a multielectrode array, demonstrating the technology’s potential for building scalable, yet energy-efficient on-node processors for brain-chip interfaces.

Live demonstration: A TiO2 ReRAM parameter extraction method

We demonstrate a desktop platform which has the ability of modeling ReRAM TiO2 samples in a highly automated manner. The system consists of a bespoke RRAM characterization instrument that hosts packaged RRAM devices and is operated via a PC. The systems python-based software includes a module that automatically applies strategically chosen sequences of pulses to a test device and then extracts the suitable parameter values for a resistive switching model from the elicited response.

Mitigating noise effects in volatile nano-metal oxide neural detector

The sensitivity of a recently proposed spike detector exploiting the volatile properties of memristive device is optimised. A 200 nm × 200 nm TiOx memristive device in volatile region is biased with sub-threshold, unipolar and bipolar neural events. The input neural signal is pre-processed using different amplification settings. The resistive state response of the test device in response to the input events is analysed and it is found that inclusion of events in positive polarity leads to subsequent increase in the number of false events when benchmarked against state-of-the-art spike detector (template matching system). The performance of the system is thereafter optimised by determining optimum amplification settings and employing an offset such that positive polarity events in the input signal are minimised.