A Highly Reliable Molybdenum Disulfide-Based Synaptic Memristor Using a Copper Migration-Controlled Structure.

Memristors are drawing attention as neuromorphic hardware components because of their non-volatility and analog programmability. In particular, electrochemical metallization (ECM) memristors are extensively researched because of their linear conductance controllability. Two-dimensional materials as switching medium of ECM memristors give advantages of fast speed, low power consumption, and high switching uniformity. However, the multistate retention in the switching conductance range for the long-term reliable neuromorphic system has not been achieved using two-dimensional materials-based ECM memristors. In this study, the copper migration-controlled ECM memristor showing excellent multistate retention characteristics in the switching conductance range using molybdenum disulfide (MoS2 ) and aluminum oxide (Al2 O3 ) is proposed. The fabricated device exhibits gradual resistive switching with low switching voltage (<0.5 V), uniform switching (σ/µ âˆ¼ 0.07), and a wide switching range (>12). Importantly, excellent reliabilities with robustness to cycling stress and retention over 104 s for more than 5-bit states in the switching conductance range are achieved. Moreover, the contribution of the Al2 O3 layer to the retention characteristic is investigated through filament morphology observation using transmission electron microscopy (TEM) and copper migration component analysis. This study provides a practical approach to developing highly reliable memristors with exceptional switching performance.

Conductive-bridging random-access memories for emerging neuromorphic computing.

With the increasing utilisation of artificial intelligence, there is a renewed demand for the development of novel neuromorphic computing owing to the drawbacks of the existing computing paradigm based on the von Neumann architecture. Extensive studies have been performed on memristors as their electrical nature is similar to those of biological synapses and neurons. However, most hardware-based artificial neural networks (ANNs) have been developed with oxide-based memristors owing to their high compatibility with mature complementary metal-oxide-semiconductor (CMOS) processes. Considering the advantages of conductive-bridging random-access memories (CBRAMs), such as their high scalability, high on-off current with a wide dynamic range, and low off-current, over oxide-based memristors, extensive studies on CBRAMs are required. In this review, the basics of operation of CBRAMs are examined in detail, from the formation of metal nanoclusters to filament bridging. Additionally, state-of-the-art experimental demonstrations of CBRAM-based artificial synapses and neurons are presented. Finally, CBRAM-based ANNs are discussed, including deep neural networks and spiking neural networks, along with other emerging computing applications. This review is expected to pave the way toward further development of large-scale CBRAM array systems.