Previous projects:  

Sensing Ultra-Low Concentrations of Biomolecules based on Quantum Controlled Sensors in Diamond (2021-2023)

Abstract- With a goal of atomic scale sensitivity in mind, we aim to develop a protocol in which the biological signal (i.e. presence of target molecule) is transduced to a magnetic signal by chemical modifications of the sensor (substrate containing NVs with appropriate surface engineering) and optical readout of the perturbation to the NV center. The core components of the sensor will be comprised of nanodiamonds (NDs) and magnetic nanoparticles modified by specific epitopes of the protein antibody. The NDs could be immobilized on the substrate (chemically, or using optical traps), and magnetic nanoparticles with optimized properties will be flowed over the NDs in a microfluidic channel. The target biomolecule will bind to the NDs and magnetic nanoparticles, forming a chain of molecules. The resulting perturbation on the NV centers in the NDs could be studied by relaxation time measurement protocols and fluorescent detection schemes. A simpler realization of such a scheme would be to avoid using NDs, and instead directly form a microfluidic channel on bulk diamond samples containing shallow NVs, with suitable surface modification for linking with the target biomolecule. We aim to start with an experimentally simpler alternative to understand the sensor performance better, and compare the performance of all optical transduction to a combination of microwave actuation and optical detection of the NVs. This technology platform will be evaluated for cancer biomarkers (e.g. PSA, IL-6 etc.) and the same scheme could be leveraged for a variety of biosensing applications with appropriate, minimal modifications.

Modelling of the Current Voltage Characteristics of Carbon Nanotube Gate All Around Field Effect Transistor.(2020-2021)

Abstract-In recent years, there has been a significant increase in research into new materials to replace existing CMOS in order to meet the current scaling requirements. In the coming days of nanotechnology, CNTFETs tend to be the most exciting technology to replace CMOS. Because of three factors, they are favoured over other complementary conventional silicon technology: First, the device's operation theory and structure are identical to those of CMOS devices, so we can use the same CMOS design infrastructure. Second, the CMOS fabrication process can be reused. The most significant explanation is that CNFET currently has the best experimentally demonstrated computer current carrying capacity. Charge distributions and the potential within the carbon nanotube were used to model current transport in carbon nanotube field effect transistors (CNT-FETs). In this work a simplified Current Voltage characteristics for single wall multi channel CNT Gate All Around field effect transistor, has been proposed based on Natori‘s model, exploiting the dependence of CNT properties on its dimension to approximate CNT density of sates. A Semiclassical approach has been adapted in this work which expects to show deep correspondence with the existing technology without the need of drastic change in present techniques to model and to fabricate integrated circuits and devices. To validate the proposed model various combinations of single wall and multi channel CNTFETs are designed and simulated using Technology Computer Aided Design (TCAD) and a comparative analysis between the analytical (MATLAB) and simulated result have been presented. The short channel matrices like Drain Induces Barrier lowering (DIBL), Subthreshold Voltage Swing (SS) and on current to off current ratio are estimated for the proposed structures. 

 Linear Predictive Analysis of Speech Signal for Application in Speech Coding (2017-2018)

Abstract- Linear Predictive Coding (LPC) is defined as a digital method for encoding an analog signal in which a particular value is predictably a linear function of the past values of the signal. Human speech is produced in the vocal tract which can be approximated as a variable diameter tube. The linear predictive coding (LPC) model is based on a mathematical approximation of the vocal tract represented by this tube of a varying diameter. At a particular time, t, the speech sample s(t) is represented as a linear sum of the ‘p’ previous samples. The most important aspect of LPC is the linear predictive filter which allows the value of the next sample to be determined by a linear combination of previous samples. Under normal circumstances, speech is sampled at 8000 samples/second with 8 bits used to represent each sample. This provides a rate of 64000 bits/second. Linear predictive coding reduces this to 3300 bits/second. However, the speech is still audible and it can still be easily understood. Since there is information loss in linear predictive coding, it is a lossy form of compression. We will describe the necessary background needed to understand how the vocal tract produces speech. We will also explain how linear predictive coding mathematically approximates the parameters of the vocal tract. The backbone of the mathematical model is developed using MATLAB.