To apply resistive random‐access memory (RRAM) to the neuromorphic system and improve performance, each cell in the array should be able to operate independently by reducing device variation. In addition, it is necessary to lower the operating current of the RRAM cell and enable gradual switching characteristics to mimic the low‐energy operations of biological. In most filamentary RRAMs, however, overshoot current occurs in the forming stage, and the RRAM shows large device variation, high operating current, and abrupt set and reset switching characteristics. Herein, the shortcomings occurring in the forming stage are overcome by introducing and optimizing an overshoot suppression layer. Consequently, the RRAM exhibits gradual switching characteristics both in the set and reset regions, thereby enabling implementation of 4‐bit multilevel operation. In addition, the forming step can be easily performed in a 16 × 16 crossbar array owing to its self‐compliance characteristics without disturbing neighboring cells in the array. The tuning and vector–matrix multiplication (VMM) operations are also experimentally verified in the array. Finally, classification performance with off‐chip training is compared in terms of accuracy and robustness to tuning tolerance depending on the number of bits of the implemented multiconductance levels.
In this study, we demonstrate both of digital and analog memory operations in InGaZnO (IGZO) memristor devices by controlling the electrode materials for neuromorphic application. The switching properties of the devices are determined by the initial energy barrier characteristics between the metal electrodes and the IGZO switching layer. Digital switching characteristics are obtained after the forming process when Schottky junction occurs at both of top and bottom electrodes. On the other hands, analog resistive switching is achieved when Schottky and Ohmic junctions exist at each side because the applied voltage modulates the Schottky barrier height through the Ohmic contact. In addition, the weightupdate properties of the devices are verified depending on identical and incremental pulse schemes. The incremental pulse trains improve the linearity and variation of weight modulation, leading to the stable learning characteristics of neuromorphic system in terms of pattern recognition with MNIST handwritten digit images.
In this work, flexible InGaZnO (IGZO) synaptic thin-film transistors (TFTs) with different gate dielectric layers are fabricated and analyzed to investigate the effect of the gate insulator of flexible IGZO synaptic TFTs in terms of weight window and retention characteristics. The gradual weight modulation of these devices comes from the migration of hydrogens in the Al 2 O 3 layer deposited by low-temperature atomic layer deposition and can be controlled by gate bias. In addition, the learning behaviors with identical and incremental pulse schemes are verified for a linear weight modulation, and its effect in pattern recognition accuracy is studied considering device variation and retention properties in a 784 × 10 fully connected neural network with handwritten digit images.
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