Integration of monolayer graphene with a semiconductor device

The integration of monolayer graphene with a semiconductor device for gas sensing applications involves obtaining a CMOS device that is prepared to receive monolayer graphene channels. After population of the monolayer graphene channels on the CMOS device, electrical contacts are formed at each end of the monolayer graphene channels with interconnect vias having sidewalls angled at less then 90°. Additional metallization pads are added at the location of the monolayer graphene channels to improve planarity and reliability of the semiconductor processing involved.Board of Regents, University of Texas Syste

Memristors Based on 2D Monolayer Materials

2D materials have been widely used in various applications due to their remarkable and distinct electronic, optical, mechanical and thermal properties. Memristive effect has been found in several 2D systems. This chapter focuses on the memristors based on 2D materials, e. g. monolayer transition metal dichalcogenides (TMDs) and hexagonal boron nitride (h-BN), as the active layer in vertical MIM (metal–insulator–metal) configuration. Resistive switching behavior under normal DC and pulse waveforms, and current-sweep and constant stress testing methods have been investigated. Unlike the filament model in conventional bulk oxide-based memristors, a new switching mechanism has been proposed with the assistance of metal ion diffusion, featuring conductive-point random access memory (CPRAM) characteristics. The use of 2D material devices in applications such as flexible non-volatile memory (NVM) and emerging zero-power radio frequency (RF) switch will be discussed

Metaplastic and Energy-Efficient Biocompatible Graphene Artificial Synaptic Transistors for Enhanced Accuracy Neuromorphic Computing

CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/m^2 switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks

Graphene-Based Biosensor for Early Detection of Iron Deficiency

Iron deficiency (ID) is the most prevalent and severe nutritional disorder globally and is the leading cause of iron deficiency anemia (IDA). IDA often progresses subtly symptomatic in children, whereas prolonged deficiency may permanently impair development. Early detection and frequent screening are, therefore, essential to avoid the consequences of IDA. In order to reduce the production cost and complexities involved in building advanced ID sensors, the devices were fabricated using a home-built patterning procedure that was developed and used for this work instead of lithography, which allows for fast prototyping of dimensions. In this article, we report the development of graphene-based field-e�ect transistors (GFETs) functionalized with anti-ferritin antibodies through a linker molecule (1-pyrenebutanoic acid, succinimidyl ester), to facilitate specific conjugation with ferritin antigen. The resulting biosensors feature an unprecedented ferritin detection limit of 10 fM, indicating a tremendous potential for non-invasive (e.g., saliva) ferritin detection

Large-signal model of 2DFETs: compact modeling of terminal charges and intrinsic capacitances

We present a physics-based circuit-compatible model for double-gated two-dimensional semiconductor based field effect transistors, which provides explicit expressions for the drain current, terminal charges and intrinsic capacitances. The drain current model is based on the drift-diffusion mechanism for the carrier transport and considers Fermi-Dirac statistics coupled with an appropriate field-effect approach. The terminal charge and intrinsic capacitance models are calculated adopting a Ward-Dutton linear charge partition scheme that guarantees charge-conservation. It has been implemented in Verilog-A to make it compatible with standard circuit simulators. In order to benchmark the proposed modeling framework we also present experimental DC and high-frequency measurements of a purposely fabricated monolayer MoS2 FET showing excellent agreement between the model and the experiment and thus demonstrating the capabilities of the combined approach to predict the performance of 2DFETs.Comment: 7 pages, 6 figure