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There is a need to design a hardware synapse array appropriate for enhancing the efficiency of neuromorphic computing systems while minimizing energy consumption. This study introduces a memristor device with an AlOx overshoot suppression layer (A‐OSL) to achieve a self‐compliance effect. By optimizing each cell within the 16 × 16 crossbar array, synaptic devices are successfully fabricated with reliable characteristics and 3‐bit multilevel capabilities. In addition, the oxygen composition of TiOx and the annealing conditions are optimized to reduce the forming voltage and minimize the variation in the switching voltage. As a result, stable forming‐free characteristics are obtained through A‐OSL insertion, a reduction in forming voltage, and TiOx oxygen composition optimization. Also, target weights are accurately transferred to the A‐OSL memristor crossbar array and conducted the inference process by applying spike signals to the array following the designated time step. The spiking neural network (SNN) is demonstrated by measuring vector‐matrix multiplication (VMM) of the 16 × 16 crossbar array. The VMM results exhibit a classification accuracy of 90.80% for the MNIST dataset, which is close to the accuracy achieved by software‐based approaches, amounting to 91.85%. [ABSTRACT FROM AUTHOR]

To analyze the effect of the intrinsic variations of the memristor device on the neuromorphic system, we fabricated 32 × 32 Al2O3/TiO x -based memristor crossbar array and implemented 3 bit multilevel conductance as weight quantization by utilizing the switching characteristics to minimize the performance degradation of the neural network. The tuning operation for 8 weight levels was confirmed with a tolerance of ±4 μ A (±40 μ S). The endurance and retention characteristics were also verified, and the random telegraph noise (RTN) characteristics were measured according to the weight range to evaluate the internal stochastic variation effect. Subsequently, a memristive neural network was constructed by off-chip training with differential memristor pairs for the Modified National Institute of Standards and Technology (MNIST) handwritten dataset. The pre-trained weights were quantized, and the classification accuracy was evaluated by applying the intrinsic variations to each quantized weight. The intrinsic variations were applied using the measured weight inaccuracy given by the tuning tolerance, RTN characteristics, and the fault device yield. We believe these results should be considered when the pre-trained weights are transferred to a memristive neural network by off-chip training. [ABSTRACT FROM AUTHOR]

Recently, various memory devices have been actively studied as suitable candidates for synaptic devices, which are important memory and computing units in neuromorphic systems. One of the ways to manage these devices is off-chip training, where it is essential to transfer the pretrained weights accurately. Previous studies, however, have a few limitations, such as a lack of consideration of program errors that occur during the transfer process. Although the smaller the program error, the higher the accuracy, the corresponding increase in the program time must be considered. To evaluate the practical applicability, we fabricated Al2O3/TiOx-based resistive random access memory (RRAM) and investigated the effect of program errors on program time and system degradation. It was confirmed that for smaller program errors, the program time was exponentially longer. Furthermore, we examined the effect of variation with respect to the number of quantized weight states (${N}_{state}$) through system-level simulation. We observed that the optimized ${N}_{state}$ varies depending on whether the program error is small or large. This result is meaningful as it experimentally shows the tradeoff between the program error, program time, and system performance. We expect it to be useful in the development of neuromorphic systems. [ABSTRACT FROM AUTHOR]

As interest in artificial intelligence (AI) and relevant hardware technologies has been developed rapidly, algorithms and network structures have become significantly complicated, causing serious power consumption issues because an enormous amount of computation is required. Neuromorphic computing, a hardware AI technology with memory devices, has emerged to solve this problem. For this application, multilevel operations of synaptic devices are important to imitate floating point weight values in software AI technologies. Furthermore, weight transfer methods to desired weight targets must be arranged for off-chip training. From this point of view, we fabricate 32 × 32 memristor crossbar array and verify the 3-bit multilevel operations. The programming accuracy is verified for 3-bit quantized levels by applying a reset-voltage-control programming scheme to the fabricated TiOx/Al2O3-based memristor array. After that, a synapse composed of two differential memristors and a fully-connected neural network for modified national institute of standards and technology (MNIST) pattern recognition are constructed. The trained weights are post-training quantized in consideration of the 3-bit characteristics of the memristor. Finally, the effect of programming error on classification accuracy is verified based on the measured data, and we obtained 98.12% classification accuracy for MNIST data with the programming accuracy of 1.79% root-mean-square-error. These results imply that the proposed reset-voltage-control programming scheme can be utilized for a precise tuning, and expected to contribute for the development of a neuromorphic system capable of highly precise weight transfer. [ABSTRACT FROM AUTHOR]

In this Letter, we present reset-voltage-dependent precise tuning operation of TiOx/Al2O3-based memristive devices. For the high resistance state (HRS) with high reset voltage, abrupt set operations are observed with a large variation, while the HRS obtained by low reset voltage provides gradual and uniform switching behaviors. The improvement of gradual switching and the programming accuracy are analyzed regarding cycle-to-cycle as well as device-to-device variations. We believe that these results can be applied to operate TiOx/Al2O3-based memristors in areas requiring highly accurate tuning characteristics. [ABSTRACT FROM AUTHOR]