Bioelectronic VertiTFET with Multilayer Nanosheet Gating for Dual-Mode, Ultra-Sensitive Stroke State Profiling

This study investigates a rapid, cost-effective method for brain stroke detection using a Recessed Drain (RD) Heterojunction (HJ) Vertically Stacked (VS), Gate-All-Around (GAA), and Nanosheet (NS) Tunneling Field-Effect Transistor (TFET) biosensor. The core innovation lies in the three vertically stacked Silicon nanosheet channels, which provide an expanded sensing surface, enhanced electrostatic control, and improved biomolecule interaction compared to single-channel devices. The proposed RD-HJ-VS-GAA-NS-TFET outperforms conventional biosensor architectures such as planar MOSFETs, multigate FETs, and nanowire FETs, due to its wider channel and multilayered design. To address strain effects arising from silicon growth on SiGe, a comparative analysis between conventional silicon and strained silicon nanosheet channels is explicitly performed. The sensitivity analysis is carried out by examining key parameters, including the Drain Current (<i>I</i>_D) response, Subthreshold Swing behavior (SS), and the switching ratio (<i>I</i>_ON/<i>I</i>_OFF). The dielectric constant exhibits significant variation between healthy and stroke-affected brain tissues due to the distinct electromagnetic properties of brain tissues, particularly when they interact with nanocavities at high frequencies. Device performance is analyzed with respect to cavity length, cavity thickness, gate work function configurations, cavity orientation, filling factor, and nonuniform step profiles. The <i>I</i>_ON/<i>I</i>_OFF ratio increases from 3.58 × 10¹¹ under hemorrhagic state (<i>k</i> = 30) to 5.40 × 10¹¹ under healthy state (<i>k</i> = 42) and further to 1.05 × 10¹² under ischemic state (<i>k</i> = 61), highlighting the improved switching characteristics of the proposed biosensor, which is crucial for effectively distinguishing between stroke-affected and healthy brain tissues. The proposed RD-HJ-VS-GAA-NS-TFET biosensor shows strong potential as a label-free, Point-Of-Care (POC) diagnostic platform for rapid and accurate stroke detection.