The relevant findings, as detailed in previous sections, regarding the device guidelines for an analog synapse using the PCMs have highlighted the potential of RRAMs. In case of the RRAMs, which generally represent devices that use oxygen vacancies (or oxygen ions) as mobile species, oxygen vacancies are created by breaking the bonds between metal and oxygen either at the bulk oxide or interface.
[35, 36] Alternatively, cations are supplied from electrodes such as Cu or Ag outside the materials, which is known as conductive-bridge RAM (CBRAM).
[52] Whether the mobile species are anions or cations, the ions driven by the applied set field are clustered, eventually bridging the two separate electrodes. Instantaneously, high current can thus be observed in the RRAM through the formation of a conductive filament. Meanwhile, as the opposite reset voltage spreads the oxygen vacancies from the filament, the filament starts to dissolve through an electrochemical reaction. The current flow is limited as the filament is disconnected. In general, a compliance current that limits excess current over a preset value is applied to the RRAM to prevent permanent breakdown. The magnitude of the compliance current directly determines the amount of current flowing through the RRAM, which implies that the size of the filament is provided. As the filament thickens by increasing the compliance currents, a lower LRS is continuously achieved. In contrast, the higher negative voltage removes more oxygen vacancies from the filament, thereby forming a switching gap between the electrode and the remaining filament. The extended gap can have multiple HRS in the lower direction.
Through using a cross-point array with only a single RRAM
[53, 54] or one-transistor and one-resistor (1T–1R) configuration,
[55-59] diverse classification and recognition features and functions have been explored and demonstrated experimentally. A two-layer perceptron has been constructed by the building of 128 × 64 Ta/HfO
x/Pt (from top to bottom) based 1T–1R arrays.
[55] The conductance toward a higher level was precisely tuned by the gate voltage of the monolithically integrated transistor. Due to the use of the two pairs of the RRAM as the single synaptic element discussed in case of the PCM, the conductance in the lower direction was achieved by first applying the reset pulse to initialize the state, and the gate voltage was thereafter increased. The tunable linear and symmetric update of the conductance with minimal variation allowed the hardware neural network to be trained properly, experimentally achieving an accuracy of 91.71% of the MNIST dataset.