NEW APPROACH TOWARDS SMART BIOSENSORS: AI-MEMRISTIVE BIO-DETECTION CENTRE FOR MICROSYSTEM TECHNOLOGY (CMST) Student: Manel Bouzouita Supervisors: Ioulia Tzouvadaki, Zayer Fakhreddine ABSTRACT Memristors offer high density, low energy consumption, memory effect capability, and advanced sensing capabilities, making them ideal for revolutionizing nonvolatile memory. We are developing a nanosensor with low power consumption that uses machine learning to accurately and efficiently detect biomolecules. Our simulations and machine learning classification models confirm the potential for a reliable and seamless AI-powered biosensing platform that could revolutionize biological system monitoring. State of Art Non-volatile memory devices • Non-volatile memory is a form of computer memory that can maintain stored data even when power is turned off. • It commonly pertains to semiconductor memory chips, containing information in floating-gate memory cells made up of floating-gate MOSFETs, which include NAND flash and solid-state drives (SSD) as flash memory storage[1]. • Memristors have the capability to remember their final state even after the current flow has been stopped, thereby rendering them a useful option for non-volatile memory[2]. Dynamic Memristor: Memristive Crossbar (MCR) structure • The formation and rupture of conductive filaments with nano-scale width are observed to occur across the entirety of the oxide layer, resulting in the desired set and reset transitions (figures 1.1 1.2 1.3 1.5). These phenomena are contingent upon the threshold voltage bias and the impact of joule heating[5]. • The MCR refers to an advanced physical model of a metal oxide memristor (figure 1.3) that draws its inspiration from Sungho Kim's research. • It considers various factors that impact resistive switching behaviors, such as electric field, temperature, oxygen vacancy concentration gradient, and material parameters [3]. Memristive Biosensing • A memristive biosensor is a non-volatile memory device that is capable of sensing while functionalized. • The silicon nanowire-based biosensor (figure 2)is composed of two back-to-back Schottky diodes with opposite functions [5]. • The metal oxide memristive biosensor is made of Metal-Insulator-Metal (MIM) capacitive resistive (MCR) architecture (figure 3) and comprises a unipolar metal oxide memristive device. AI advanced Biosensing • Employing supervised machine learning techniques in the context of memristive biosensors encompasses several key aspects, with output labelling being a pivotal attribute. • This process enables the categorization of sensor output data into distinct groups, contingent upon the specific model and encoding scheme chosen for the analysis. • This preprocessing involves a sequence of stages, including: – Data extraction from the memristive biosensor. – Treatments such as filtration and scaling. These preparatory measures are instrumental in ensuring that the data fed into the algorithm is: devoid of noise, exhibits high reliability, and offers the utmost accuracy, • The ML intervention for biosensing has a corrective capability of enhancing the sensor's specificity and bolstering its ability to detect anomalies, consequently underlining the promise of delivering high specificity and anomaly-free detection outcomes[6]. System Overview: AI-Based Memristive Biosensing Primary Results And Discussion Results of Dynamic MCR modelling: Figure 5 Results of ML modelling: Table 1 Contact Manel.Bouzouita@ugent.be www.ugent.be/elis Universiteit Gent @ugent Ghent University Acknowledgement The research endeavor was made possible thanks to generous financial support from the esteemed University of Monastir and the distinguished Faculty of Sciences of Monastir, Tunisia. Such funding has allowed for the pursuit of valuable knowledge and advancements in the respective fields of study. Fig.(2) Fig.(1) References [1] ‘Non-volatile memory’, Wikipedia. Sep. 07, 2023. Accessed: Oct. 02, 2023. [Onlin