In field-effect transistor (FET) biosensors, the interfaces between solid and liquid are crucial, as sensors detect changes in the surface potential (ψ0) by analyte charges. Some of these charges are screened by ions in electrolyte solutions; thus, only a fraction of the charges induce opposite charges in the FET. Electrolytic screening is commonly described by the Debye-Hückel (DH) model based on the Poisson-Boltzmann theory. However, the DH model is only valid when ψ0 is smaller than ψL = |kBT/ze|, where e is the elementary charge and z is the valency of the bulk electrolyte; for example, ψL = 26 mV for monovalent electrolyte solutions. When ψ0 exceeds ψL, the DH model becomes less precise, and the excess charge density in the electrical double layer (EDL) varies nonlinearly with ψ0, resulting in enhanced charge screening, known as nonlinear screening. This is evident from Eq. 1, which represents the FET-sensitivity toward the binding analyte charge (σana). The highest sensitivity is achieved when ψ0 approaches zero (an uncharged surface), whereas the sensitivity decreases as ψ0 increase. Therefore, it is imperative to engineer ψ0 and surface charge in FET biosensors. There have been several reports on engineering ψ0 and surface charges in FET biosensors to improve their sensitivity. Shoorideh et al. modified the surface charge at the solid/liquid interface of a Si FET using both front and back gate. More recently, Gupta et al. modulated the negatively charged SiO2 surface of Si FETs by depositing positively charged blocking molecules, improving sensitivity by 51%. Among the various types of FET biosensors, graphene FET (GFET) biosensors have attracted particular attention owing to their extremely high electron/hole mobilities and their two-dimensional (2D) nature, which provide a highly sensitive 2D sensing platform. Numerous studies have reported the ability of GFET biosensors to detect biomolecules, including proteins, small antigens, exosomes, and viruses. However, relatively less emphasis has been placed on the engineering of ψ0 and surface charge in GFET biosensors. In this study, we modified the surface charge of graphene and demonstrated biosensing using surface-charge-modulated GFETs (SCM-GFETs) to enhance sensitivity. Many studies have indicated that the graphene surface is negatively charged. For instance, Bepete et al. showed that the ζ-potential of graphene was ζ = -45 mV under neutral pH conditions. Zuccaro et al. reported that the isoelectric point (pI) of unmodified graphene was less than 3.3, indicating that graphene is negatively charged at neutral pH conditions. Negative charges on graphitic particles were also observed in other studies. The negative charge of graphene primarily arises from the structure and the water molecules at the EDL and specific adsorption of OH -ions. Although the value of the ζ-potential of graphene may fluctuate depending on the fabrication process and substrates present underneath the graphene, these studies imply that signals of GFETs without surface-charge modulation may be hindered by nonlinear electrolytic screening; thus, counterbalancing the surface charge of graphene is deemed essential in GFET biosensors. The negatively charged graphene surface can be altered by decoration with positively charged molecules. For example, Zuccaro et al. showed that the value of pI increased by up to 7.5 through consecutive electrochemical modification using 4aminobenzylamine. To modify the surface charge, we employed NR3 + polystyrene (PS) beads with a ζ-potential of 1.8 mV. GFET biosensors specifically detect the target analyte by depositing the quaternary ammonium compounds (QACs) in conjunction with a capture probe. It is noted that FET sensors respond not only to analyte charges but also to environmental variations such as ionic strength, pH, and temperature changes. These changes may result in baseline drift, making it challenging to analyze the sensor response. We integrated non-functionalized GFETs next to the capture probe functionalized GFETs and performed differential measurements to cancel the response to background changes and extract net signals of the target. Additionally, as GFET characteristics suffer from FET-to-FET variability due to process variations, we integrated multiple GFETs into the chips, and calculated the average values of the GFET characteristics to improve the measurement reliability. Furthermore, target molecules that were fluorescently labeled on the devices were visualized after electrical measurements using a fluorescence microscope. It is necessary to verify that the obtained GFET signal was a result of the specific adsorption of the target onto the antibodies. Fabrication of GFET Arrays. The devices were fabricated as follows; detailed information is available in the literature. The GFETs were fabricated by transferring CVD-graphene onto a Si/SiO2 substrate with a thickness of 525 μm/290 nm. The source/drain electrode lines were formed with 10 nm Ti and 90 nm Au using electron-beam evaporation. The graphene films were patterned into an array of six rows and nine columns using a 300 × 300 μm 2 mask via oxygen plasma etching, resulting in 54 GFETs (#1-#54) integrated into the chips. The channel width and length of each GFET were 100 μm. It was confirmed that the graphene films were not torn or contaminated using an optical microscope (DSX510, Olympus) and a deep-learning method. The GFET array was divided into two regions (Region 1: #1-#24 and Region 2: #31-#54) using a silicone rubber placed on the chip. The GFETs (#25-#30) were covered with rubber and, thus, were not used for analysis.