Due to the increasing importance of artificial intelligence (AI), significant recent effort has been devoted to the development of neuromorphic circuits that seek to emulate the energy‐efficient information processing of the brain. While non‐volatile memory (NVM) based on resistive switches, phase‐change memory, and magnetic tunnel junctions has shown potential for implementing neural networks, additional multi‐terminal device concepts are required for more sophisticated bio‐realistic functions. Of particular interest are memtransistors based on low‐dimensional nanomaterials, which are capable of electrostatically tuning memory and learning behavior at the device level. Herein, a conceptual overview of the memtransistor is provided in the context of neuromorphic circuits. Recent progress is surveyed for memtransistors and related multi‐terminal NVM devices including dual‐gated floating‐gate memories, dual‐gated ferroelectric transistors, and dual‐gated van der Waals heterojunctions. The different materials systems and device architectures are classified based on the degree of control and relative tunability of synaptic behavior, with an emphasis on device concepts that harness the reduced dimensionality, weak electrostatic screening, and phase‐changes properties of nanomaterials. Finally, strategies for achieving wafer‐scale integration of memtransistors and multi‐terminal NVM devices are delineated, with specific attention given to the materials challenges for practical neuromorphic circuits.
Memtransistors In article number 2108025, Vinod K. Sangwan, Mark C. Hersam, and co‐workers review recent advances in memtransistor devices and circuits to highlight their unique gate‐tunable attributes in the context of neuromorphic computing. This work also outlines remaining challenges and future research directions, such as wafer‐scale and 3D integration, that will benefit the progression of memtransistors and other multiterminal nonvolatile memory devices for use in practical neuromorphic circuits. The image depicts a crossbar array of memtransistors, which is one of the leading architectures for neuromorphic hardware.
Liquid metals are ideally suited for flexible and wearable electronics due to their compatibility with additive manufacturing and high electrical conductivity that is maintained following mechanical perturbation. While printing of eutectic gallium–indium (eGaIn) liquid metal nanoparticles has been demonstrated, previous techniques for activating electrical conductivity in the as‐printed insulating eGaIn nanoparticles limit throughput in roll‐to‐roll manufacturing processes. Here, ultrafast photonic sintering of eGaIn nanoparticles is demonstrated, which is further enhanced through the use of nitrocellulose as a carrier polymer that undergoes optically triggered combustion to produce eGaIn thin films with electrical conductivities exceeding 104 S cm–1. This combustion‐assisted photonic sintering (CAPS) is two orders of magnitude faster than previously demonstrated noncontact sintering techniques. By circumventing the established tradeoff between electrical conductivity and activation speed, CAPS will facilitate the use of eGaIn liquid metal nanoparticles in high‐throughput additive manufacturing of flexible and wearable electronics, sensors, and related technologies.
Solution‐processed graphene is a promising material for numerous high‐volume applications including structural composites, batteries, sensors, and printed electronics. However, the polydisperse nature of graphene dispersions following liquid‐phase exfoliation poses major manufacturing challenges, as incompletely exfoliated graphite flakes must be removed to achieve optimal properties and downstream performance. Incumbent separation schemes rely on centrifugation, which is highly energy‐intensive and limits scalable manufacturing. Here, cross‐flow filtration (CFF) is introduced as a centrifuge‐free processing method that improves the throughput of graphene separation by two orders of magnitude. By tuning membrane pore sizes between microfiltration and ultrafiltration length scales, CFF can also be used for efficient recovery of solvents and stabilizing polymers. In this manner, life cycle assessment and techno‐economic analysis reveal that CFF reduces greenhouse gas emissions, fossil energy usage, water consumption, and specific production costs of graphene manufacturing by 57%, 56%, 63%, and 72%, respectively. To confirm that CFF produces electronic‐grade graphene, CFF‐processed graphene nanosheets are formulated into printable inks, leading to state‐of‐the‐art thin‐film conductivities exceeding 104 S m−1. This CFF methodology can likely be generalized to other van der Waals layered solids, thus enabling sustainable manufacturing of the diverse set of applications currently being pursued for 2D materials.
Sulfur-deficient polycrystalline two-dimensional (2D) molybdenum disulfide (MoS2) memtransistors exhibit gate-tunable memristive switching to implement emerging memory operations and neuromorphic computing paradigms. Grain boundaries and sulfur vacancies are critical for memristive switching; however, the underlying physical mechanisms are not fully understood. Furthermore, the adsorption of water and gaseous species strongly perturbs electronic transport in monolayer MoS2, and little work has been done to explore the influence of surface interactions on defect-related kinetics that produces memristive switching. Here, we study the switching kinetics of back-gated MoS2 memtransistors using current transient measurements in a controlled atmosphere chamber. We observe that adsorbed water molecules lead to suppression of the electronic trap-filling processes concomitant with the resistive switching process, resulting in altered kinetics of the resistive switching. Additionally, using the transient response from “bunched” drain voltage pulse trains performed as a function of temperature, we extract the energy of the affected trap state and find that it places the trap roughly midgap [ET=EC – 0.7 (±0.4) eV]. Our results highlight the importance of controlling for surface interactions that may affect switching kinetics in 2D memtransistors, synaptic transistors, and related memory devices.
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