High power radio frequency (RF) transfer-doped diamond field effect transistors (FETs) are being fabricated at the Army Research Laboratory (ARL). To implement these into radar systems we have a parallel effort to extract accurate compact models from their measured DC and RF data. At this early stage we are using the commercially available Angelov model and will discuss fitting the model parameters and how their parameter values differ from GaN and GaAs FETs. Results indicate good model prediction of measured results in some cases. Also, model extraction can indicate areas of the device that needs greater attention for improved performance such as the access region resistance. Furthermore, in the saturation region of operation these transistors exhibit a hole saturation velocity of 5 × 106 cm/s obtained from extracted model parameters.
Army Research Laboratory (ARL) is developing radio frequency (RF) field-effect-transistors (FETs) on hydrogen-terminated, single-crystal diamond surfaces. By employing advanced fabrication methods, we achieve state-of-the-art device performance with gate lengths below 100 nm. We are exploring methods to improve the stability of fabricated FETs, which is critical for maturation of the technology and its commercial acceptance. DC and RF measurement data will be reviewed and discussed within the framework of improving device yield and reliability.
Surface induced transfer doping (SITD) is a novel, highly efficiency doping technique that is being used to invoke the p-type surface conductivity of intrinsic diamond for high-frequency, high-power electronic devices. In the SITD process, a high electron affinity (EA) thin film acceptor layer is interfaced with the hydrogenated diamond surface with negative electron affinity (NEA) to induce the effective p-type doping on the diamond surface. Overall, device performance of the SITD doped devices is contingent on the type and quality of the interface between the acceptor layer and hydrogenated diamond surfaces. Motivated by this, our internal theoretical modeling efforts based on a hybrid approach of machine learning and first principle calculations have focused on performing bottom-up design of novel acceptor layers with higher stability and improved device performance, e.g., doped TMOs and 2D layer. In this talk, recent results from our predictive modeling effort will be presented.
The results are presented of the study of intracellular localization of Co-phthalocyanines. The methods were used of Raman and fluorescence microspectroscopy.
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