Beyond use as high density non-volatile memories, memristors have potential as synaptic components of neuromorphic systems. We investigated the suitability of tantalum oxide (TaOx) transistor-memristor (1T1R) arrays for such applications, particularly the ability to accurately, repeatedly, and rapidly reach arbitrary conductance states. Programming is performed by applying an adaptive pulsed algorithm that utilizes the transistor gate voltage to control the SET switching operation and increase programming speed of the 1T1R cells. We show the capability of programming 64 conductance levels with <0.5% average accuracy using 100 ns pulses and studied the trade-offs between programming speed and programming error. The algorithm is also utilized to program 16 conductance levels on a population of cells in the 1T1R array showing robustness to cell-to-cell variability. In general, the proposed algorithm results in approximately 10× improvement in programming speed over standard algorithms that do not use the transistor gate to control memristor switching. In addition, after only two programming pulses (an initialization pulse followed by a programming pulse), the resulting conductance values are within 12% of the target values in all cases. Finally, endurance of more than 10(6) cycles is shown through open-loop (single pulses) programming across multiple conductance levels using the optimized gate voltage of the transistor. These results are relevant for applications that require high speed, accurate, and repeatable programming of the cells such as in neural networks and analog data processing.
Applications of memristor devices are quickly moving beyond computer memory to areas of analog and neuromorphic computation. These applications require the design of devices with different characteristics from binary memory, such as a large tunable range of conductance. A complete understanding of the conduction mechanisms and their corresponding state variable(s) is crucial for optimizing performance and designs in these applications. Here we present measurements of low bias I–V characteristics of 6 states in a Ta/ tantalum-oxide (TaOx)/Pt memristor spanning over 2 orders of magnitude in conductance and temperatures from 100 K to 500 K. Our measurements show that the 300 K device conduction is dominated by a temperature-insensitive current that varies with non-volatile memristor state, with an additional leakage contribution from a thermally-activated current channel that is nearly independent of the memristor state. We interpret these results with a parallel conduction model of Mott hopping and Schottky emission channels, fitting the voltage and temperature dependent experimental data for all memristor states with only two free parameters. The memristor conductance is linearly correlated with N, the density of electrons near EF participating in the Mott hopping conduction, revealing N to be the dominant state variable for low bias conduction in this system. Finally, we show that the Mott hopping sites can be ascribed to oxygen vacancies, where the local oxygen vacancy density responsible for critical hopping pathways controls the memristor conductance.
We propose and demonstrate a novel physical computing paradigm based on an engineered unipolar memristor that exhibits symmetric SET switching with respect to voltage polarity. A one-dimensional array of these devices was sufficient to demonstrate an efficient Hamming distance comparator for two strings of analog states represented by voltages from the physical world. The comparator first simultaneously applies the two sets of voltages to the array of memristors, each of which is initially in its high resistance state and switches to its low resistance state only if the two voltages applied on that memristor differ by more than the switching threshold. An accurate analog representation of the Hamming distance is then obtained by applying a reading voltage to the memristors and summing all the resultant currents. The comparator with a small footprint can directly process analog signals and store computation results without power, representing a promising application for analog computing based on memristor crossbar arrays.
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