Ali Khiat

Memristive devices as parameter setting elements in programmable gain amplifiers

In this paper, we investigate the AC performance of a variable gain amplifier that utilizes an in-house manufactured memristor as a gain setting element. Analysis includes frequency and phase responses as the memristor is programmed at different resistive states. A TiO2-based solid-state memristor was employed in the feedback branch of an inverting voltage amplifier and was programmed externally. We have also observed indications of memcapacitive effects and a correlation with resistive states is presented. We demonstrate that our TiO2 memristive devices, although possessing relatively low ROFF/RON switching ratios (∼10), are versatile and can be used reliably in programmable gain amplifiers.

A Memristor SPICE Model Accounting for Volatile Characteristics of Practical ReRAM

Realizing large-scale circuits utilizing emerging nanoionic devices known as memristors depends on the accurate modeling of their behavior under a wide range of biasing conditions. Currently, no available SPICE memristor model accounts for both nonvolatile and volatile resistive switching characteristics, the coexistence of which has been recently demonstrated to manifest on practical ReRAM. In this letter, we present a new memristor SPICE model that introduces volatile effects, which can render a rate-dependent bipolar nonvolatile switching operation. The model is demonstrated via a number of simulation cases and is benchmarked against measured results acquired by solid-state TiO2 ReRAM.

Temporal processing with volatile memristors

Short-term synaptic plasticity (STP) is a mechanism identified in brain systems according to which the effective connection strength (synaptic strength) between two neurons varies dynamically with recent communication history. As a consequence, the amplitude of the post-synaptic potential in response to a single pre-synaptic event, so-called “spike”, may increase (short-term facilitation) or decrease (short-term depression) with consecutive presynaptic stimulation. However, in contrast to Long-term Synaptic plasticity, these changes are temporary and are typically restored in the absence of input. Interestingly, however, a single neuron which receives input via both facilitating and depressing synapses has improved discrimination capability, distinguishing, for instance, between a sequence of events and a sequence of the same events presented in the reversed order. We, therefore, studied the memory mechanisms in emerging non-CMOS devices with a view to application in temporal pattern recognition and detection, inspired by the STP mechanisms. In particular, we demonstrate that memristors can exhibit a resembling behavior to STP due to an inherent volatility and hysteresis. When stimulated by closely spaced pulse waves, the conductance of the device decreases similar to what a depressing synapse would do if presented with consecutive pre-synaptic spikes. This work paves the way for employing memristors in solving spatio-temporal sequence learning problems.

Qualitative SPICE modeling accounting for volatile dynamics of TiO2 memristors

Accurate modeling of memristive devices is a critical condition that will allow the realization of large-scale memristor based circuits. The current methodology regarding modeling focuses on obtaining realistic pinched hysteresis curves, which are memristor signatures, but these do not hold useful information regarding device performance. We divert from this practice and propose a SPICE memristor model constructed based on qualitative verified assumptions of real memristive device operation. Our model introduces volatile effects that render a rate-dependent operation, and also accounts for both bipolar and unipolar switching. We demonstrate its plausibility via a wealth of simulation cases, which are qualitatively similar to several memristor dynamics reported in literature. Finally our model is benchmarked against measured results acquired by solid-state TiO2 memristors.