MOSFET Insight: AI-driven Predictive Analytics for VLSI Design

Welcome to MOSFET Insight! This project leverages AI algorithms to predict threshold characteristics and I/O characteristics of MOSFETs, crucial for VLSI design.

Overview

MOSFET Insight uses machine learning to predict key characteristics of MOSFETs, such as threshold voltage and drain current, based on a variety of physical and electrical parameters. This predictive capability is valuable for optimizing VLSI designs, improving manufacturing processes, and enhancing the reliability of semiconductor devices.

Project Repository

GitHub Repository

Features and Labels

In this project, we use supervised learning with the following feature columns and label columns:

Feature Columns

1. Physical Dimensions and Materials:

   • gate_length: Length of the MOSFET gate (L)
   • gate_width: Width of the MOSFET gate (W)
   • oxide_thickness: Thickness of the gate oxide (T_ox)
   • channel_material_properties: Properties of the channel material (e.g., type of material, bandgap)

2. Doping Parameters:

   • source_doping_concentration: Doping concentration of the source (N_s)
   • drain_doping_concentration: Doping concentration of the drain (N_d)
   • channel_doping_concentration: Doping concentration of the channel

3. Operating Conditions:

   • supply_voltage: Supply voltage (V_dd)
   • temperature: Operating temperature (T)

4. Electrical Characteristics:

   • gate_capacitance: Gate capacitance (C_g)
   • subthreshold_slope: Subthreshold slope (S)
   • mobility: Carrier mobility (μ)

5. Environmental Factors:

   • temperature_variation: Variation in temperature
   • noise_factors: External noise factors

Label Columns

1. Threshold Voltage (Regression Target):

   • threshold_voltage: The threshold voltage of the MOSFET (V_th)

2. Drain Current (Regression Target):

   • drain_current: The drain current of the MOSFET (I_d)

Usefulness of the Project

MOSFET Insight provides several key benefits for the semiconductor industry and VLSI design:

• Predictive Modeling Under Variability: It models the impact of manufacturing and process variability on MOSFET characteristics, providing more accurate predictions than static spec sheets.
• Design Optimization: The tool can optimize MOSFET design parameters to achieve desired performance metrics, reducing the need for extensive physical testing.
• Reliability and Aging Predictions: It predicts long-term reliability and potential degradation of MOSFET performance under different conditions, aiding in the design of durable devices.
• Complex System Behavior: It helps predict how MOSFETs will perform within complex circuits, considering interactions between multiple components.
• Rapid Prototyping and Simulation: By speeding up simulations, the project enables quicker design iterations and prototyping.
• Advanced Analytics: The project can uncover data-driven insights and detect anomalies in MOSFET performance, improving overall design quality.

Getting Started

1. Clone the repository:

   git clone https://github.com/mdjannatulnayem/MOSFET-Insight.git

2. Navigate to the project directory:

   cd MOSFET-Insight

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

Follow the instructions in the notebooks directory to load the dataset, train the machine learning model, and make predictions on new data.

Contributing

We welcome contributions to improve MOSFET Insight. Please fork the repository and submit pull requests.

License

This project is licensed under the Apache-2.0 License.

Contact

For any questions or inquiries, please contact [[email protected]].

This README file provides a comprehensive overview of the project, specifying the feature columns and label columns for the regression problem, and highlighting the usefulness of the project in the context of VLSI design and semiconductor manufacturing.