Tailoring\nConductive MXene@MOF Interfaces: New Generation\nof Synapse Devices for Neuromorphic Computing
Synapse\ndevices, pivotal components in neuromorphic computing,\ndemonstrate unique properties that are essential for advanced computing\nsystems. These devices, characterized by their metal/resistive layer/metal\nstructure, rely heavily on active layer material. One important challenge\nin developing synapse devices for artificial neural networks lies\nin constructing these networks at a hardware level to achieve in-memory\ncomputing, enabling the efficient processing of information while\nminimizing power consumption. Herein, we present a rational design\nand in situ synthesis of two-dimensional (2<i>D</i>/2D)\nheteronanostructures intricately integrating Ti-based metal carbide\nas Ti-MXene (Ti<sub>3</sub>C<sub>2</sub>) with copper-based metal–organic\nframework as Cu-tetrakis (4-carboxyphenyl) porphyrin (Cu-TCPP) through\nvan der Waals interactions to form a hybrid as [Ti<sub>3</sub>C<sub>2</sub>@Cu-TCPP] (1). The hybrid exhibits synergistic properties\nof both counterparts with an intricate hierarchical structure, ensuring\nexceptional stability and remarkable conductivity, fundamental for\nthe progression of advanced neuromorphic devices. The resultant hybrids\nshow an advanced neuromorphic device with comprehensive comparative\nanalysis using DC I–V sweeps was conducted to evaluate different\ndevice types, focusing on parameters such as the high-resistance state,\nlow-resistance state, and on/off ratio. Results demonstrated that\nTi<sub>3</sub>C<sub>2</sub>@Cu-TCPP@PVA-based devices exhibited an\nimpressive on/off ratio of approximately 10<sup>2</sup>, outperforming\nCu-TCPP@PVA and Ti<sub>3</sub>C<sub>2</sub>@PVA-based devices. This\nhighlights the superior performance of Ti<sub>3</sub>C<sub>2</sub>@Cu-TCPP@PVA and its potential for advanced applications in neural\nnetwork systems. Furthermore, the conduction mechanism was elucidated,\nrevealing the dominance of the space-charge limited conduction mechanism\nduring the SET process and the Schottky emission mechanism during\nthe RESET process.
Publication Details
- Journal
- Figshare
- Published
- 2024-08-19
- DOI
- https://doi.org/10.1021/acs.chemmater.4c01596.s001
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00