18591680. PARAMETER SEARCH METHOD simplified abstract (SEMICONDUCTOR ENERGY LABORATORY CO., LTD.)
Contents
PARAMETER SEARCH METHOD
Organization Name
SEMICONDUCTOR ENERGY LABORATORY CO., LTD.
Inventor(s)
Takahiro Fukutome of Atsugi (JP)
PARAMETER SEARCH METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 18591680 titled 'PARAMETER SEARCH METHOD
Simplified Explanation:
This patent application introduces a method for selecting a parameter candidate for a semiconductor element by utilizing a combination of measurement data, parameter extraction, circuit simulation, and neural network optimization.
Key Features and Innovation:
- Utilizes measurement data to extract a model parameter for a semiconductor element.
- Employs a classification model to learn and classify the model parameter.
- Utilizes a neural network to adjust a variable in the circuit simulation process.
- Updates the neural network weight coefficient based on simulation output results.
- Determines the best candidate parameter based on simulation output satisfaction conditions.
Potential Applications: This technology can be applied in the semiconductor industry for optimizing semiconductor element parameters, improving circuit simulation accuracy, and enhancing overall semiconductor device performance.
Problems Solved:
- Efficient parameter selection for semiconductor elements.
- Enhanced accuracy in circuit simulation.
- Optimization of semiconductor device performance.
Benefits:
- Improved semiconductor device performance.
- Enhanced circuit simulation accuracy.
- Streamlined parameter selection process.
Commercial Applications: Optimizing semiconductor device parameters for various applications such as consumer electronics, automotive systems, and industrial machinery can lead to improved product performance and reliability in the market.
Prior Art: Readers interested in exploring prior art related to this technology can start by researching semiconductor parameter optimization methods, circuit simulation techniques, and neural network applications in semiconductor device design.
Frequently Updated Research: Stay updated on the latest advancements in semiconductor parameter optimization, circuit simulation methodologies, and neural network applications in semiconductor device design to ensure the most current and relevant information in this field.
Questions about Semiconductor Parameter Optimization: 1. How does this technology improve semiconductor device performance compared to traditional methods? 2. What are the key advantages of using neural networks in optimizing semiconductor element parameters?
Original Abstract Submitted
A parameter candidate for a semiconductor element is provided. A data set of measurement data is provided to a parameter extraction portion, and a model parameter is extracted. A first netlist is provided to a circuit simulator, simulation is performed using the first netlist and the model parameter, and a first output result is output. A classification model learns the model parameter and the first output result and classifies the model parameter. A second netlist and a model parameter are provided to the circuit simulator. A variable to be adjusted is supplied to a neural network, an action value function is output, and the variable is updated. The circuit simulator performs simulation using the second netlist and the model parameter. When a second output result to be output does not satisfy conditions, a weight coefficient of the neural network is updated. When the second output result satisfies the conditions, the variable is judged to be the best candidate.
