RESUMO
We enhanced the device uniformity for reliable memory performances by increasing the device surface roughness by exposing the HfO2 thin film surface to argon (Ar) plasma. The results showed significant improvements in electrical and synaptic properties, including memory window, linearity, pattern recognition accuracy, and synaptic weight modulations. Furthermore, we proposed a non-identical pulse waveform for further improvement in linearity accuracy. From the simulation results, the Ar plasma processing device using the designed waveform as the input signals significantly improved the off-chip training and inference accuracy, achieving 96.3% training accuracy and 97.1% inference accuracy in only 10 training cycles.
RESUMO
Single-walled carbon nanotubes have exceptional electronic properties and have been proposed as a replacement for silicon in applications such as low-cost thin-film transistors and high-performance logic devices. However, practical devices will require dense, aligned arrays of electronically pure nanotubes to optimize performance, maximize device packing density and provide sufficient drive current (or power output) for each transistor. Here, we show that aligned arrays of semiconducting carbon nanotubes can be assembled using the Langmuir-Schaefer method. The arrays have a semiconducting nanotube purity of 99% and can fully cover a surface with a nanotube density of more than 500 tubes/µm. The nanotube pitch is self-limited by the diameter of the nanotube plus the van der Waals separation, and the intrinsic mobility of the nanotubes is preserved after array assembly. Transistors fabricated using this approach exhibit significant device performance characteristics with a drive current density of more than 120 µA µm(-1), transconductance greater than 40 µS µm(-1) and on/off ratios of â¼1 × 10(3).
