Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Semiconductors

Semiconductors

There is variation in the electrical conductivity of materials - metals, semiconductors, and insulators that are showcased with the help of the energy band diagrams.
Metals such as copper (Cu), zinc (Zn), or lead (Pb) have low resistivity and feature conduction bands that are either not fully occupied or overlap with the valence band, making a bandgap non-existent. This allows electrons in the highest energy levels of the valence band to easily transition to the conduction band upon gaining...

You might also read

Related Articles

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

Sort by

Same author

Engineering gold-platinum core-shell nanoparticles by self-limitation in solution.

Communications chemistry·2023

Same author

Self-Limiting Shell Formation in Cu@Ag Core-Shell Nanocrystals during Galvanic Replacement.

The journal of physical chemistry letters·2020

Same author

Manipulating the Transition Dipole Moment of CsPbBr<sub>3</sub> Perovskite Nanocrystals for Superior Optical Properties.

Nano letters·2019

Same author

Design Principles for Trap-Free CsPbX<sub>3</sub> Nanocrystals: Enumerating and Eliminating Surface Halide Vacancies with Softer Lewis Bases.

Journal of the American Chemical Society·2018

Same author

Atomic Layer Etching: Rethinking the Art of Etch.

The journal of physical chemistry letters·2018

Same author

Tailoring Morphology of Cu-Ag Nanocrescents and Core-Shell Nanocrystals Guided by a Thermodynamic Model.

Journal of the American Chemical Society·2018

Search research articles

Related Experiment Video

Updated: Nov 25, 2025

Mechanostimulation of Multicellular Organisms Through a High-Throughput Microfluidic Compression System

Mechanostimulation of Multicellular Organisms Through a High-Throughput Microfluidic Compression System

Published on: December 23, 2022

Human-machine collaboration for improving semiconductor process development.

Keren J Kanarik¹, Wojciech T Osowiecki¹, Yu Joe Lu¹

¹Lam Research Corporation, Fremont, CA, USA.

|March 8, 2023

Summary

Artificial intelligence (AI) and human engineers can jointly develop semiconductor chip processes more affordably. Combining human expertise with AI algorithms halves development costs compared to human-only approaches.

More Related Videos

Operation of the Collaborative Composite Manufacturing CCM System

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

Silicon Microchips for Manipulating Cell-cell Interaction

Silicon Microchips for Manipulating Cell-cell Interaction

Published on: August 30, 2007

Related Experiment Videos

Last Updated: Nov 25, 2025

Mechanostimulation of Multicellular Organisms Through a High-Throughput Microfluidic Compression System

Mechanostimulation of Multicellular Organisms Through a High-Throughput Microfluidic Compression System

Published on: December 23, 2022

Operation of the Collaborative Composite Manufacturing CCM System

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

Silicon Microchips for Manipulating Cell-cell Interaction

Silicon Microchips for Manipulating Cell-cell Interaction

Published on: August 30, 2007

Area of Science:

Materials Science
Computer Science
Engineering

Background:

Semiconductor chip development relies on costly chemical plasma processes.
Manual development by engineers is time-consuming and expensive.
Limited experimental data hinders accurate atomic-scale predictive modeling.

Purpose of the Study:

Investigate artificial intelligence (AI) for reducing semiconductor process development costs.
Benchmark human engineers against AI algorithms in a virtual process design game.
Explore hybrid human-AI strategies for cost-efficient chip fabrication.

Main Methods:

Utilized Bayesian optimization algorithms.
Created a controlled virtual process game for systematic benchmarking.
Compared performance metrics of human designers and AI algorithms.

Main Results:

Human engineers are effective in initial development stages.
AI algorithms demonstrate superior cost-efficiency near target tolerances.
A hybrid human-AI strategy reduced cost-to-target by 50% compared to human-only design.

Conclusions:

AI offers significant cost reductions in semiconductor process development.
Hybrid human-AI collaboration is a promising strategy for efficient chip fabrication.
Cultural integration challenges must be addressed for successful AI implementation in industry.