Straightforward bias- and frequency-dependent small-signal model extraction for single-layer graphene FETs

Exploiting Ambipolarity in Graphene Field‐Effect Transistors for Novel Designs on High‐Frequency Analog Electronics

“…Graphene Technology Electrical and Physical TCAD Parameters for the Power Amplifier: The graphene technology considered for the power amplifier design fits (not shown in this work) the reported technology developed by the Institut d'Electronique de Microélectronique et de Nanotechnologie (CNRS, Lille, France) reported elsewhere and described in the following. [54,[77][78][79] The device fabrication starts with the application of e-beam lithography on a 300 nm SiO 2 /highly resistive Si substrate to pattern the gate. Next, a 40-nm layer of Al was deposited and lifted off.…”

“…This can be done, as far as the small-signal regime is applicable, in terms of a linear network of lumped elements. In Figure 1c, the charge-based small-signal equivalent circuit of the GFET (derived and validated against HF measurements of different GFET technologies) [54,55] is shown. The elements that form the equivalent circuit are calculated from the voltage derivatives of both the current (I DS ) and terminal charges (Q G , Q D , and Q S ), by accounting for the charge conservation and non-reciprocity of the intrinsic capacitances of the GFET.…”

An Aging Small-Signal Model for Degradation Prediction of Microwave Heterojunction Bipolar Transistor S-Parameters Based on Prior Knowledge Neural Network

“…In recent years, research on the conventional small-signal model has focused on the characterization of non-ideal effects corresponding to ultra-high frequency rates and severe parasitics [6][7][8]. The reliability modeling-related research on this basis has been insufficient, still focusing mainly on the physical level of the device, with constraints such as long R&D cycle time, slow modeling, and incompatibility with EDA softwares [9,10].…”