We analyze the response of identical pulses on a filamentary resistive memory (RRAM) to implement the synapse function in neuromorphic systems. Our findings show that the multilevel states of conductance are achieved by varying the measurement conditions related to the formation and rupture of a conductive filament. Furthermore, abrupt set switching behavior in the RRAM leads to an unchanged conductance state, leading to degradation in the accuracy of pattern recognition. Thus, we demonstrate a linear potentiation (or depression) behavior of conductance under identical pulses using the effect of barrier layer on the switching, which was realized by fabricating an RRAM on top of an Al electrode. As a result, when the range of the conductance is symmetrically controlled at both polarities, a significantly improved accuracy is achieved for pattern recognition using a neural network with a multilayer perceptron.

We propose a HfZrO _x (HZO)-based ferroelectric synapse device with multi-levels states of remnant polarization that is equivalent to multi-levels conductance states. By optimizing the pulse condition, we obtained 32 levels of remnant polarization states for both potentiation and depression. Furthermore, a ferroelectric field-effect transistor is simulated using the obtained multiple remnant polarization states. The simulation results show that linear and symmetric conductance states can be obtained by applying optimum potentiation and depression pulse conditions. A neural network was simulated using the proposed devices for pattern recognition. Using synapse parameters of the HZO-based ferroelectric device and a neural network simulator, we have confirmed that the pattern recognition accuracy of the MNIST data set is 84%. It shows that the HZO-based synapse device has potential for future high-density neuromorphic systems.

We propose TiO _x -based resistive switching device for neuromorphic synapse applications. This device is capable of 64-levels conductance states because of their optimized interface between the metal electrode and the TiOx film. To compensate the change in switching power with increasing pulse number, we propose the use of fixed voltage and current pulses in potentiation and depression conditions, respectively. By adopting a hybrid pulse scheme, the symmetry of conductance change under both potentiation and depression conditions is shown to be significantly improved. Both the improved conductance levels and the symmetry of conductance change are directly related with enhanced pattern recognition accuracy, which is confirmed by a neural network simulation.

We perform a comparative study of HfO ₂ and Ta ₂ O ₅ resistive switching memory (RRAM) devices for their possible application as electronic synapses. By means of electrical characterization and simulations, we link their electrical behavior (digital or analog switching) to the properties and evolution of the conductive filament (CF). More specifically, we identify that bias-polarity-dependent digital switching in HfO ₂ RRAM is primarily related to the creation and rupture of an oxide barrier. Conversely, the modulation of the CF size in Ta ₂ O ₅ RRAM allows bias-polarity-independent analog switching with multiple states. Therefore, when the Ta ₂ O ₅ RRAM is used to implement a synapse in multilayer perceptron neural networks operated by back-propagation algorithms, patterns in handwritten digits can be recognized with high accuracy.

In this brief, we demonstrate the multilevel cell (MLC) characteristics of an HfO ₂ -based resistive memory (RRAM) array as a synaptic element for neuromorphic systems. We utilize various programming schemes to linearly change the resistance state with either set voltage/pulse ramping or gate voltage ramping. Our results reveal that the MLC relates to the size of the conductive filament involved in the movement of oxygen vacancies with respect to applying pulses. Thus, by optimizing the pulse for a set condition, such as an identical pulse, we achieve linearly increased MLC behavior, thereby enabling a high accuracy for pattern recognition in neuromorphic systems.