Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Network Function of a Circuit01:25

Network Function of a Circuit

322
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
322
Neuroplasticity01:01

Neuroplasticity

568
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
568
Neural Circuits01:25

Neural Circuits

1.3K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.3K
MOS Capacitor01:25

MOS Capacitor

839
A Metal-Oxide-Semiconductor (MOS) capacitor is a fundamental structure used extensively in semiconductor device technology, particularly in the fabrication of integrated circuits and MOSFETs (metal-oxide-semiconductor field-effect transistors). The MOS capacitor consists of three layers: a metal gate, a dielectric oxide, and a semiconductor substrate.
The metal gate is typically made from highly conductive materials such as aluminum or polysilicon. Beneath the metal gate lies a thin layer of...
839

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Meta-Learned and TCAD-Assisted Sampling in Semiconductor Laser Annealing.

ACS omega·2023
Same author

An ultra-compact blackbody using electrophoretic deposited carbon nanotube films.

RSC advances·2022
Same author

Mapping Oxidation and Wafer Cleaning to Device Characteristics Using Physics-Assisted Machine Learning.

ACS omega·2022
See all related articles

Related Experiment Video

Updated: Jul 19, 2025

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits
10:32

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Published on: April 15, 2015

8.5K

Neuroevolution-Based Network Architecture Evolution in Semiconductor Manufacturing.

Yen-Wei Feng1, Bing-Ru Jiang1, Albert Shihchun Lin1

  • 1Institute of Electronics Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan.

ACS Omega
|August 14, 2023
PubMed
Summary
This summary is machine-generated.

Neuroevolution (NE) automatically optimizes model architectures for intelligent manufacturing, outperforming traditional multilayer perceptron (MLP) models in semiconductor process modeling. This approach efficiently integrates physical constraints without requiring domain expertise.

More Related Videos

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions
07:38

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

1.6K
Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model
09:47

Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model

Published on: October 18, 2015

10.1K

Related Experiment Videos

Last Updated: Jul 19, 2025

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits
10:32

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Published on: April 15, 2015

8.5K
Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions
07:38

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

1.6K
Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model
09:47

Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model

Published on: October 18, 2015

10.1K

Area of Science:

  • Materials Science
  • Computer Science
  • Electrical Engineering

Background:

  • Developing accurate models for intelligent manufacturing, especially in semiconductor fabrication, is challenging due to complex physical processes and the need for extensive trial-and-error.
  • Traditional methods often struggle to embed domain knowledge and physical constraints effectively into model architectures.
  • Semiconductor manufacturing involves intricate steps like oxidation, etching, implantation, annealing, diffusion, and chemical-mechanical polishing, each requiring precise modeling.

Purpose of the Study:

  • To automatically search for optimized model architectures using a neuroevolution-based approach for semiconductor manufacturing.
  • To develop a model that can rapidly build suitable networks while integrating practical physical constraints without explicit domain knowledge extraction.
  • To compare the performance of the neuroevolution (NE) model against a traditional multilayer perceptron (MLP) model for predicting silicon-germanium (SiGe) heterojunction bipolar transistor collector current.

Main Methods:

  • Utilized a neuroevolution (NE) approach to automatically discover optimal model architectures.
  • Employed Technology Computer-Aided Design (TCAD) generated data for silicon-germanium (SiGe) heterojunction bipolar transistor collector current as the target dataset.
  • Used six key process parameters (oxidation, dry/wet etching, implantation, annealing, diffusion, chemical-mechanical polishing) as model inputs.

Main Results:

  • The NE model achieved significantly lower error metrics compared to the MLP model.
  • Mean square errors for the NE model were 3.285 × 10-7 (train) and 1.661 × 10-7 (validation), versus 1.317 × 10-6 and 7.215 × 10-7 for MLP.
  • Mean absolute percentage error on the test set was 0.097 for NE, substantially better than 0.216 for MLP, indicating superior predictive accuracy and better physical insight extraction.

Conclusions:

  • Neuroevolution-based model architecture search offers a promising and efficient method for intelligent manufacturing applications in semiconductor fabrication.
  • The NE approach successfully integrates physical constraints and achieves faster turnaround times compared to traditional methods.
  • The study demonstrates that NE models possess a superior ability to extract physical insights from data compared to conventional MLP models.