RESUMEN
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 AlOx/TiOy, 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, AlOx/TiOy 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.
RESUMEN
This study focuses on InGaZnO-based synaptic devices fabricated using reactive radiofrequency sputtering deposition with highly uniform and reliable multilevel memory states. Electron trapping and trap generation behaviors were examined based on current compliance adjustments and constant voltage stressing on the ITO/InGaZnO/ITO memristor. Using O2 + N2 plasma treatment resulted in stable and consistent cycle-to-cycle memory switching with an average memory window of ~95.3. Multilevel resistance states ranging from 0.68 to 140.7 kΩ were achieved by controlling the VRESET within the range of -1.4 to -1.8 V. The modulation of synaptic weight for short-term plasticity was simulated by applying voltage pulses with increasing amplitudes after the formation of a weak conductive filament. To emulate several synaptic behaviors in InGaZnO-based memristors, variations in the pulse interval were used for paired-pulse facilitation and pulse frequency-dependent spike rate-dependent plasticity. Long-term potentiation and depression are also observed after strong conductive filaments form at higher current compliance in the switching layer. Hence, the ITO/InGaZnO/ITO memristor holds promise for high-performance synaptic device applications.
RESUMEN
Bipolar gradual resistive switching was investigated in ITO/InGaZnO/ITO resistive switching devices. Controlled intrinsic oxygen vacancy formation inside the switching layer enabled the establishment of a stable multilevel memory state, allowing for RESET voltage control and non-degradable data endurance. The ITO/InGaZnO interface governs the migration of oxygen ions and redox reactions within the switching layer. Voltage-stress-induced electron trapping and oxygen vacancy formation were observed before conductive filament electroforming. This device mimicked biological synapses, demonstrating short- and long-term potentiation and depression through electrical pulse sequences. Modulation of post-synaptic currents and pulse frequency-dependent short-term potentiation were successfully emulated in the InGaZnO-based artificial synapse. The ITO/InGaZnO/ITO memristor exhibited spike-amplitude-dependent plasticity, spike-rate-dependent plasticity, and potentiation-depression synaptic learning with low energy consumption, making it a promising candidate for large-scale integration.