Memristive Architectures Exploiting Self-Compliance Multilevel Implementation on 1 kb Crossbar Arrays for Online and Offline Learning Neuromorphic Applications.

This paper suggests the practical implications of utilizing a high-density crossbar array with self-compliance (SC) at the conductive filament (CF) formation stage. By limiting the excessive growth of CF, SC functions enable the operation of a crossbar array without access transistors. An AlO/TiO, internal overshoot limitation structure, allows the SC to have resistive random-access memory. In addition, an overshoot-limited memristor crossbar array makes it possible to implement vector-matrix multiplication (VMM) capability in neuromorphic systems. Furthermore, AlO/TiO structure optimization was conducted to reduce overshoot and operation current, verifying uniform bipolar resistive switching behavior and analog switching properties. Additionally, extensive electric pulse stimuli are confirmed, evaluating long-term potentiation (LTP), long-term depression (LTD), and other forms of synaptic plasticity. We found that LTP and LTD characteristics for training an online learning neural network enable MNIST classification accuracies of 92.36%. The SC mode quantized multilevel in offline learning neural networks achieved 95.87%. Finally, the 32 × 32 crossbar array demonstrated spiking neural network-based VMM operations to classify the MNIST image. Consequently, weight programming errors make only a 1.2% point of accuracy drop to software-based neural networks.