Search

As the demand for bio‐inspired neuromorphic systems grows, memristor has emerged as a pivotal component in artificial synaptic devices. This study delves into the advantages and limitations of the one‐transistor‐one‐resistor (1T‐1R) and 0T‐1R architectures for memory array configurations. A significant enhancement in the memristor's on/off ratio, surpassing 103, achieved by integrating both an overshoot suppression layer (OSL) and an ultra‐thin AlN oxygen barrier layer (OBL) is also reported. The concurrent insertion of an AlOx OSL is essential for manifesting the self‐compliance attribute in 4F2 memristor arrays. Evaluations of a 24 × 24 array embedded with OSL and OBL reveal a substantial reduction in retention variation. In terms of functionality, a 7.13‐fold decline in vector‐matrix multiplication error is observed, accentuating the potential of this approach for neural network synapse applications. The analog reset characteristics of the OBL memristor facilitate over 25 multilevel states. Furthermore, the adoption of nonnegative weights presents an avenue to potentially double synaptic integration density. Through simulation program with integrated circuit emphasis simulations, the necessity of nonnegative and 16‐level quantized conductance in balancing power consumption without accuracy loss at the image classification is validated. [ABSTRACT FROM AUTHOR]

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]

In this study, oxygen‐rich TiOy and TiOx layers are intentionally designed to have different oxygen compositions, functioning as an overshoot suppression layer (OSL) and oxygen reservoirs. Furthermore, by natural oxidation reactions occurring between the TiOy/TiOx/Al2O3 switching layer and the Pt/Al top electrode, an additional AlOy layer can be induced to act as an additional OSL. The proposed annealing process accelerates the oxidation reaction of AlOy/TiOy OSLs, thereby enhancing the self‐compliance feature of devices. Moreover, the ultrathin AlN serves as an oxygen barrier layer (OBL) that inhibits the movement of oxygen ions at the interface between the Al2O3 layer and the Pt/Ti bottom electrode. The optimized devices are tested by DC sweep and pulses for neuromorphic computing systems. To realize biological synapse characteristics, several key synaptic memory plasticities are proposed. Finally, a 24 × 24 crossbar array based on the 0T‐1R structure, incorporating optimized AlOy/TiOy OSLs and OBL via the annealing process, is characterized. During the electroforming step, all specified target cells (marked with the letters “ESDL”) achieved self‐compliance at low current levels without experiencing hard‐breakdown failures or interference among neighboring cells. The successful array performance is demonstrated by the accurate tuning of target weights. [ABSTRACT FROM AUTHOR]