Radiation-hardened property of single-walled carbon nanotube film-based field-effect transistors under low-energy proton irradiation

Strong C-C bonds,nanoscale cross-section and low atomic number make single-walled carbon nanotubes(SWCNTs)a potential candidate material for integrated circuits(ICs)applied in outer space.However,very little work combines the simulation calculations with the electrical measurements of SWCNT field-effect transistors(FETs),which limits further understanding on the mechanisms of radiation effects.Here,SWCNT film-based FETs were fabricated to explore the total ionizing dose(TID)and displacement damage effect on the electrical performance under low-energy proton irradiation with different fluences up to 1×1015 p/cm2.Large negative shift of the threshold voltage and obvious decrease of the on-state current verified the TID effect caused in the oxide layer.The stability of the subthreshold swing and the off-state current reveals that the displacement damage caused in the CNT layer is not serious,which proves that the CNT film is radiation-hardened.Specially,according to the simulation,we found the displacement damage caused by protons is different in the source/drain contact area and channel area,leading to varying degrees of change for the contact resistance and sheet resistance.Having analyzed the simulation results and electrical measurements,we explained the low-energy proton irradiation mechanism of the CNT FETs,which is essential for the construction of radiation-hardened CNT film-based ICs for aircrafts.

Discovery of high-entropy ceramics via machine learning

Although high-entropy materials are attracting considerable interest due to a combination of useful properties and promising applications,predicting their formation remains a hindrance for rational discovery of new systems.Experimental approaches are based on physical intuition and/or expensive trial and error strategies.Most computational methods rely on the availability of sufficient experimental data and computational power.Machine learning(ML)applied to materials science can accelerate development and reduce costs.In this study,we propose an ML method,leveraging thermodynamic and compositional attributes of a given material for predicting the synthesizability(i.e.,entropy-forming ability)of disordered metal carbides.