Department of Electrical and Electronics Engineering

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Author: search.filters.author.Bhatt, Upendra MohanSubject: search.filters.subject.Machine learning (ML)

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Now showing 1 - 4 of 4
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    Automated Designing of Single Stage Operational Amplifier and Its Teleportation Among Different Technology Nodes
    (IEEE, 2020-06) Bhatt, Upendra Mohan
    Analog circuits play a vital role in advanced electronic systems and they cannot be replaced because of their need to provide an interface with the natural analog world. In this paper we have presented an analog design flow for Automated Designing and Teleportation (ADT) of analog circuits, Further ADT for a single-stage operational amplifier (single-stage op-Amp, i.e., differential amplifier) has been presented to demonstrate the complete process flow which provides accurate results in a shorter span of time using the trained machine learning models.
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    Application of Deep Neural Networks for Weed Detection and Classification
    (IEEE, 2023-06) Bhatt, Upendra Mohan
    Weeds compete for natural resources both in forest areas, harming the development of native vegetation, and in agricultural areas, affecting crop quality. The need then arises to classify these species, so that mechanical or chemical methods can be applied appropriately to contain the pests. This research presents the application and comparison of machine learning techniques, with the aim of automating the classification of images for agricultural challenges, such as the detection of defective seeds, and weeds and the category between these and native vegetation, while finally, the architecture of a convolutional neural network is presented. As a differential, the network's self-learning ability stands out, as images are captured in less than ideal conditions at varying heights and lighting levels in most cases. This research is expected to provide important information on artificial intelligence techniques that can be used in the classification of weed images, a factor that will contribute to the forestry and agricultural sector.
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    Optimal Machine Learning Model for the Relationship Between Grain Size, Channel Thickness, and Grain Boundary Trap Density in 3D NAND Strings
    (IEEE, 2024-01) Bhatt, Upendra Mohan
    The rapid growth of the semiconductor industry has resulted in the development of three-dimensional NAND (3D NAND) strings, which offer increased memory density and greater performance over classical planar NAND designs. In this work, we look at the critical aspects that determine the performance and reliability of 3D NAND strings. Using simulation results and machine learning algorithms we focus on the effects of grain size, channel thickness, and grain boundary trap density. We address their individual and cumulative effects on memory cell behavior, with the current state-of-the-art strategies used to optimize these characteristics. This work will be helpful in the ongoing development of 3D NAND flash technology.
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    A machine learning framework for predictive electron density modelling to enhance 3D NAND flash memory performance
    (Elsevier, 2024-12) Bhatt, Upendra Mohan
    Data storage in electronic devices has been revolutionised by 3D NAND flash memory. However, polycrystalline silicon and grain boundaries offer issues that greatly affect memory performance in terms of string current and Program-Erase Threshold Voltage window (Vt –Window). Scientists need to learn more about how grain size, channel thickness, and trap density affect electron behaviour to improve the efficiency of memory chips. Regression models are used in this work to forecast fluctuations in electron density along the channel in 3D NAND string devices. The dataset, which was derived using TCAD simulations, has a sizable number of samples that show the electron density as a function of channel length. We assess their performance using R2 scores and RMSE values using regression models such as Linear Regression, Random Forest, K-Neighbour Regressor, Decision Tree, Gradient Boosting, XGBRegressor, CatBoosting Regressor, and AdaBoost Regressor. By improving our knowledge of how electrons behave in transistor channels, this work contributes to the optimisation of 3D NAND flash memory.