Convolutional kernel with PrCaMnOx-based resistive random-access memory for neuromorphic image processing

Abstract

This paper investigated the conductance-state stability of TiN/PrCaMnOx (PCMO)-based resistive random-access memory (RRAM), which serves as a kernel weight element in convolutional neural networks (CNNs), to realize accurate feature extraction from images. On application of the initial forming process that actively drives more oxygen ions to form an interfacial layer between TiN and PCMO to RRAM devices with a high voltage of ±4 V, resistive switching behavior with a noticeable memory window was observed. However, the achieved conductance states continued to decrease during repeated cycling. The oxidation at the interface tended to occur thermodynamically, implying an increase in interfacial layer thickness. Considering the hardware implementation of the kernel weight matrix, with specifically assigned conductance values of the RRAM, state instability in the RRAM renders image edge detection difficult, eventually degrading the overall recognition accuracy of the CNN. Thus, we introduced an asymmetric programming voltage method, wherein a higher set voltage of −3 V than a reset voltage of +2.5 V can shift more oxygen ions back into PCMO. Consequently, when the RRAM devices programmed to different states were maintained without degradation in the 1 K cross-point array, eight clearly distinct weighted sum currents were demonstrated in the 3 × 1 subarray. Based on the measurement results, we performed feature extraction in CNN algorithms through MATLAB simulation, demonstrating input image edge detection with a high accuracy of 92%.

Manganite memristive devices: recent progress and emerging opportunities

“…Scaling up to larger networks enables the emulation of complex neural architectures and computational tasks. While some efftors have been reported in the fabrication of crossbar arrays [38,39,67,86], there still open challenges such as addressing interconnectivity, synchronization, and scalability within the network design. Successful development of larger networks will enable the exploration of advanced functionalities, including pattern recognition, learning, and inference, within the realm of neuromorphic systems.…”