Cheng Siong Lim

Analytical modeling of glucose biosensors based on carbon nanotubes

In recent years, carbon nanotubes have received widespread attention as promising carbon-based nanoelectronic devices. Due to their exceptional physical, chemical, and electrical properties, namely a high surface-to-volume ratio, their enhanced electron transfer properties, and their high thermal conductivity, carbon nanotubes can be used effectively as electrochemical sensors. The integration of carbon nanotubes with a functional group provides a good and solid support for the immobilization of enzymes. The determination of glucose levels using biosensors, particularly in the medical diagnostics and food industries, is gaining mass appeal. Glucose biosensors detect the glucose molecule by catalyzing glucose to gluconic acid and hydrogen peroxide in the presence of oxygen. This action provides high accuracy and a quick detection rate. In this paper, a single-wall carbon nanotube field-effect transistor biosensor for glucose detection is analytically modeled. In the proposed model, the glucose concentration is presented as a function of gate voltage. Subsequently, the proposed model is compared with existing experimental data. A good consensus between the model and the experimental data is reported. The simulated data demonstrate that the analytical model can be employed with an electrochemical glucose sensor to predict the behavior of the sensing mechanism in biosensors.

Enhanced Device and Circuit-Level Performance Benchmarking of Graphene Nanoribbon Field-Effect Transistor against a Nano-MOSFET with Interconnects

Comparative benchmarking of a graphene nanoribbon field-effect transistor (GNRFET) and a nanoscale metal-oxide-semiconductor field-effect transistor (nano-MOSFET) for applications in ultralarge-scale integration (ULSI) is reported. GNRFET is found to be distinctly superior in the circuit-level architecture. The remarkable transport properties of GNR propel it into an alternative technology to circumvent the limitations imposed by the silicon-based electronics. Budding GNRFET, using the circuit-level modeling software SPICE, exhibits enriched performance for digital logic gates in 16 nm process technology. The assessment of these performance metrics includes energy-delay product (EDP) and power-delay product (PDP) of inverter and NOR and NAND gates, forming the building blocks for ULSI. The evaluation of EDP and PDP is carried out for an interconnect length that ranges up to 100 μm. An analysis, based on the drain and gate current-voltage (- and -), for subthreshold swing (SS), drain-induced barrier lowering (DIBL), and current on/off ratio for circuit implementation is given. GNRFET can overcome the short-channel effects that are prevalent in sub-100 nm Si MOSFET. GNRFET provides reduced EDP and PDP one order of magnitude that is lower than that of a MOSFET. Even though the GNRFET is energy efficient, the circuit performance of the device is limited by the interconnect capacitances.

Performance evaluation of 14 nm FinFET-based 6T SRAM cell functionality for DC and transient circuit analysis

As the technology node size decreases, the number of static random-access memory (SRAM) cells on a single word line increases. The coupling capacitance will increase with the increase of the load of word line, which reduces the performance of SRAM, more obvious in the SRAM signal delay and the SRAM power usage. The main purpose of this study is to investigate the stability and evaluate the power consumption of a 14nm gate length FinFET-based 6T SRAM cell functionality for direct current (DC) and transient circuit analysis, namely, in resistor-capacitor (RC) delay. In particular, Berkeley Short-channel IGFET Model-Common Multigate (BSIM-CMG) model is utilized. The simulation of the SRAM model is carried out in HSPICE based on 14nm process technology. A shorted-gate (SG) mode FinFET is modeled on a silicon on insulator (SOI) substrate. It is tested in terms of functionality and stability. Then, a functional SRAM is simulated with 5GHz square wave at the input of word line (WL). Ideal square wave and square wave with 100 RC, 5 RC, 1 RC, and 0.5 RC are asserted to the WL and the bit lines (BL&BLB) of SRAM. Voltage at node q and q is observed. The simulation shows that 1 RC is the minimum square wave that will store correct value in node q and node q. Thus, this discovery from the research can be used as a modeling platform for circuit designers to explore and improve the SRAM tolerance against RC delay.

Design and performance analysis of 1-bit FinFET full adder cells for subthreshold region at 16 nm process technology

The scaling process of the conventional 2D-planar metal-oxide semiconductor field-effect transistor (MOSFET) is now approaching its limit as technology has reached below 20 nm process technology. A new nonplanar device architecture called FinFET was invented to overcome the problem by allowing transistors to be scaled down into sub-20 nm region. In this work, the FinFET structure is implemented in 1-bit full adder transistors to investigate its performance and energy efficiency in the subthreshold region for cell designs of Complementary MOS (CMOS), Complementary Pass-Transistor Logic (CPL), Transmission Gate (TG), and Hybrid CMOS (HCMOS). The performance of 1-bit FinFET-based full adder in 16-nm technology is benchmarked against conventional MOSFET-based full adder. The Predictive Technology Model (PTM) and Berkeley Shortchannel IGFET Model-Common Multi-Gate (BSIM-CMG) 16 nm low power libraries are used. Propagation delay, average power dissipation, power-delayproduct (PDP), and energy-delay-product (EDP) are analysed based on all four types of full adder cell designs of both FETs. The 1-bit FinFET-based full adder shows a great reduction in all four metric performances. A reduction in propagation delay, PDP, and EDP is evident in the 1-bit FinFET-based full adder of CPL, giving the best overall performance due to its high-speed performance and good current driving capabilities.

Metal oxide-graphene field-effect transistor: interface trap density extraction model

Summary A simple to implement model is presented to extract interface trap density of graphene field effect transistors. The presence of interface trap states detrimentally affects the device drain current–gate voltage relationship I ds–V gs. At the moment, there is no analytical method available to extract the interface trap distribution of metal-oxide-graphene field effect transistor (MOGFET) devices. The model presented here extracts the interface trap distribution of MOGFET devices making use of available experimental capacitance–gate voltage C tot–V gs data and a basic set of equations used to define the device physics of MOGFET devices. The model was used to extract the interface trap distribution of 2 experimental devices. Device parameters calculated using the extracted interface trap distribution from the model, including surface potential, interface trap charge and interface trap capacitance compared very well with their respective experimental counterparts. The model enables accurate calculation of the surface potential affected by trap charge. Other models ignore the effect of trap charge and only calculate the ideal surface potential. Such ideal surface potential when used in a surface potential based drain current model will result in an inaccurate prediction of the drain current. Accurate calculation of surface potential that can later be used in drain current model is highlighted as a major advantage of the model.