Conventional computing architectures are poor suited to the unique workload demands of deep learning, which has led to a surge in interest in memory-centric computing. Herein, a trilayer (Hf0.8Si0.2O2/Al2O3/Hf0.5Si0.5O2)-based self-rectifying resistive memory cell (SRMC) that exhibits (i) large selectivity (ca. 104), (ii) two-bit operation, (iii) low read power (4 and 0.8 nW for low and high resistance states, respectively), (iv) read latency (<10 μs), (v) excellent non-volatility (data retention >104 s at 85 °C), and (vi) complementary metal-oxide-semiconductor compatibility (maximum supply voltage ≤5 V) is introduced, which outperforms previously reported SRMCs. These characteristics render the SRMC highly suitable for the main memory for memory-centric computing which can improve deep learning acceleration. Furthermore, the low programming power (ca. 18 nW), latency (100 μs), and endurance (>106) highlight the energy-efficiency and highly reliable random-access memory of our SRMC. The feasible operation of individual SRMCs in passive crossbar arrays of different sizes (30 × 30, 160 × 160, and 320 × 320) is attributed to the large asymmetry and nonlinearity in the current-voltage behavior of the proposed SRMC, verifying its potential for application in large-scale and high-density non-volatile memory for memory-centric computing.

In this study, highly reliable and accurate weight‐modification behaviors are realized using a W/Al2O3 (3 nm)/HfO2 (7 nm)/TiN memristive device. The accuracy of the simulated inference of the MNIST dataset when considering the weight‐modification behavior is ≈95%. It is determined the optimal programming voltage pulsing conditions considering i) a high linearity in the weight‐modification, ii) symmetry between potentiation and depression, and iii) an alleviation of the voltage‐driving circuit overhead for the related part of weight‐modification process. Particular emphasis is placed on the last concern, and thus, the fixed shape of each programming pulse for both potentiation and depression are utilized. The optimal pulse design is 500 µs for the pulse rising, plateau, and falling times and a 2 V amplitude at the absolute scale. Additionally, the nonparametric method to evaluate the linearity and symmetry as opposed to the application of several parametric methods are proposed. The nonparametric method is based on an evaluation of actual data rather than models, and thus considers the actual variability in the conductance change, which is otherwise often ignored in the parameter optimization process.