Tunable sub-threshold current firing via insulator-to-metal transition enabled by lithographic nanochannels for neuromorphic applications

•Demonstrated on-demand control of the insulator-to-metal transition (IMT) in VO2 nanochannels, significantly reducing the threshold voltage from 5.6 V to 2.8 V for neuromorphic nanoelectronics applications.•Integrated ultrafast volatile switches (on/off ratio >10,000) and memory storage, achieving switching speeds around 180 ns, through local probe lithography in a single device.•Enhanced IMT device applications by incorporating dynamic memory capability, moving beyond switch-like functions to in-material data processing.•Confirmed memory behavior through persistent metallic domains and preferential IMT pathways along nanochannels, using optical and Kelvin probe force microscopy.•Advanced ultrasmall, high-speed, and energy-efficient conventional and neuromorphic nanoelectronics by demonstrating noise-resilient input pattern classification using interconnected coplanar nanochannels. The electric field-driven insulator-to-metal transition (IMT) offers a promising platform for developing controllable, futuristic neuromorphic nanoelectronics. However, the volatile nature of IMT, typically stimulated by a specific threshold voltage, limits its potential use primarily to switch-like applications. To broaden its applications, including in-material data processing, achieving on-demand IMT activation with dynamic memory capability is essential. This study demonstrates on-demand modulation of IMT behavior using spatially confined VO2 nanochannels, designed by local probe lithography. This approach enables the integration of ultrafast (∼180 ns) volatile switches (on/off ratio >103) and memory storage, from short- to long-term, in a single device. Notably, the threshold voltage was effectively reduced from 5.6 V to 2.8 V by precisely modulating the width of spatially embedded VO2 nanochannels. The observed memory behavior is attributed to persistent metallic domains and preferential IMT along these channels, as confirmed by optical and Kelvin probe force microscopy. Furthermore, the ability to classify input patterns, even in the presence of noise, was demonstrated using interconnected coplanar nanochannels by leveraging the short-term memory characteristics of the IMT. This report marks a significant step towards on-demand nanoscale manipulation of the IMT dynamics, laying the groundwork for ultrasmall, high-speed, and energy-efficient conventional and neuromorphic nanoelectronics. [Display omitted]