A novel Alzheimer detection rapid-testing low-cost technique by a gate engineered gate stack dual-gate FET device

Highlights

• A new FET based method has been reported to detect the AD from the target specimen.
• By means of device attribute distinction, the method enables rapid AD detection.
• The present technique uses SPME obtained grey matter sample instead of biomarkers.
• The single metal device offers more drain current fluctuation than other variants.
• The tri metal device offers more change in on-off current ratio than other variants.

Abstract

This study explores a quick, low-cost method to detect Alzheimer's disease (AD) by evaluating the accomplishment of a Gate-Stack (GS) Field Effect Transistor (FET). We investigate Single-Metal (SM), Dual-Metal (DM), and Tri-Metal Double Gate (DG) configurations, where cavities have been created by etching the oxide layer underneath the gate to immobilize grey matter samples collected through Solid-phase microextraction (SPME). Healthy and AD-affected grey matter have different dielectric characteristics at high frequencies. The dielectric constant of the etched nanocavities changes when the sample, which was formerly filled with air, is immobilized in the nanocavities. The alteration in the device drain current as well as performance at 2.4 GHz has been connected to the specimen's modified dielectric constant. To distinguish between the grey matter samples from AD patients and healthy individuals, the $I_{ON}/I_{OFF}$ of the suggested device along with the variation in device drain current, has been utilized as the foundation for the identification. The SM configuration has been examined by varying the cavity orientation and gate oxide stacking. To monitor the functioning of the suggested devices, the gate metal of the DM and TM devices has been altered, and a comparison has been made between SM, DM, and TM structures. The other recorded work from literature has been compared with the suggested detection technique. To ascertain whether the sample is impacted by AD, the proposed method can be used as a point of care (POC) diagnosis.

Introduction

1A neurological condition known as Alzheimer's disease (AD) can cause dementia and mental health problems in sufferers [1,2]. Alzheimer's disease (AD), which mainly affects older persons, is a degenerative cognitive and memory loss condition that can be lethal [3]. Presumably the most frequent kind of neurodegenerative illness, AD ranks sixth in the US in terms of causes of death [4,5]. In the United Kingdom, AD ranks second for males and first for women in terms of cause of death [6]. By 2050, one out of every 85 people are expected to have AD [7]. There are reportedly no effective medications or treatments that can halt or reverse the progression of AD [8]. If suitable curing techniques are not devised to treat or prevent AD, ageing populations in wealthy nations guarantee that the disease will spread like an epidemic [9]. The majority of experimental treatments for AD have not improved clinical outcomes, most likely because they were introduced too late in the course of the illness [10]. The need for biomarkers to identify AD in its early stages is highlighted by the possibility that these treatments might be more successful if administered prior to severe brain damage [11]. Early identification can help families plan for the patient's care, emotional, and financial needs, monitor responses, and direct more successful therapies.

The most recent advancements in AD clinical diagnosis necessitate specialized clinics and involve a physical examination, blood test, neuroimaging, neuropsychological testing, and cerebrospinal fluid (CSF) study. AD cannot be detected early using existing diagnostic techniques like computed tomography (CT) and magnetic resonance imaging (MRI) [12]. Molecular imaging of brain deposits of amyloid by positron emission tomography (PET) and the use of approved biomarkers, such as those generated from beta amyloid in CSF, are recommended to facilitate timely diagnosis [[13], [14], [15]]. While there have been breakthroughs in this field, the development of amyloid-based biomarkers and tests for early AD detection is hampered by two key factors [[16], [17], [18]]. The aetiology and mechanisms of disease pathology are poorly understood by amyloid-based biomarkers [[19], [20], [21]]. Moreover, evaluations based on these biomarkers cannot identify those susceptible to AD before there is a noticeable buildup of amyloid-beta in the brain. Throughout the course of the disease, biomarkers that can detect biological events before brain amyloid-beta development (amyloid-pathology) are required. These indicators could improve knowledge of the condition, make it easier to identify people when the illness is still in its early stages, and help create novel treatments. According to studies, AD is marked by changes in metabolism [13], which may occur before amyloid pathology [21]. Consequently, signals of such metabolic abnormalities may function as early-stage disease biomarkers rather than amyloid biomarkers. These indicators can be extracted from blood because of its great metabolic information richness. Numerous studies have attempted to uncover non-amyloid biomarkers for disease by examining a broad variety of non-amyloid protein in the blood and searching for their link with the illness [22]; however, this method is challenging to use in real-world settings.

Instead of any biomarker, grey matter has been considered as the sample used to identify AD in this study. The progressive accretion of amyloid beta plaques, tau neurofibrillary tangles is the main histological change in the brain during AD [23]. Beta-amyloid proteins are amorphous clusters that condense into plaques in the brain, impairing signal transmission between synapses [24]. The relative permittivity of samples of brain tissue from Alzheimer's disease patients and healthy brain tissue samples differs at microwave frequency because of the existence of amyloid beta protein in the brain [25]. In the healthy brain, grey matter has a relative permittivity of 54.263 at 2.4 GHz, whereas in the brain of an AD patient, it is 42.939 [26]. AD patients can be identified by comparing the relative permittivity of healthy and AD-affected samples. Because they can detect changes in electrical characteristics, FET-based biosensors which are renowned for their small size, high sensitivity, and simplicity are perfect for point-of-care screening.

A device that detects ions, the FET-based biosensor was initially introduced by Bergveld in 1970 [27]. The literature has reported on a number of ion-sensitive devices, and it has been found that charged biomolecules function better in these devices [[28], [29], [30]]. Numerous research has been published in the literature that employ the dielectric alteration of the biomolecules to sense the neutral biomolecule with greater sensitivity [31,32]. A number of FET-based biosensors were recently developed to identify illnesses. It has been documented that the array of FET sensors can be used to recognize biomarkers for bladder cancer, such as cytokeratin 8 (CK8) and NMP22 [33,34]. A CNT-based model detects prostate-specific antigen, or PSA, in samples of urine to detect prostate cancer early [35]. The literature has proposed using a FinFET structure to measure the device's conductivity to identify malignant breast cells [36]. The several tumour biomarker for cancer in lung, neuron-specific enolase or NSE followed by cytokeratin fragment 21-1 or CYFRA 21-1, can be detected label-free using a FET biosensor, as reported by Cheng et al. [37]. Tunnel FET (TFET) related biosensors have been published in the literature to recognize different biomolecules such as malignant liver cell [[38], [39], [40], [41], [42], [43]]. Several studies have been published using FET-based biosensors to identify AD. Bungon et al. reported on the creation and assessment of graphene FET (GFET) biosensors for the detection of Clusterin, an acknowledged protein biomarker of AD [44]. To detect the primary blood biomarker of AD, β-amyloid (Aβ), a high-sensitivity carbon nanotube (CNT) biosensor based on aptamer, has been published in the literature [45]. Park et al. showcase the use of a reduced graphene oxide FET (gFET) to achieve highly accurate and multiplexed identification of critical AD biomarkers (t-Tau, Aβ_1-42) in bio-fluids [46]. Some more FET-based biosensors have been reported to detect the Aβ and Tau proteins for the detection of AD [[47], [48], [49]]. Several studies on FET-based work have been published in the literature, demonstrating its use as a POC detection device for the identification of various diseases [[50], [51], [52], [53], [54]].

Several biomarkers have been sensed in the reported literature to identify AD, but brain cells, i.e., the grey matter, have been considered as the sample in this proposed work. Solid-phase microextraction (SPME), as defined by Bogusiewicz et al., was utilized to gather the study sample [55]. The sample can be inserted into the cavities of the gate stack dual-gate FET for detection once it has been extracted. Silvaco Atlas technology computer-aided design (TCAD) has been used in the suggested work [56]. The suggested devices' operational efficiency has been examined in this work by adjustments to the cavity orientation, gate stacking, and gate work function. Sections 2 Device architecture, 3 Results and discussion of this paper detail the device topology and the sensitivity assessment of the device's numerous controlling parameters, respectively. A comparative study of the three suggested structures is also included in Section 3. The proposed approach of detection has been evaluated with various FET-based biosensors from the literature in Section 4.