权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

SBIR Phase II: Disruptive Semiconductor Software Tool for Recipe Optimization for Deposition and Etching Processes

SBIR 第二阶段：用于沉积和蚀刻工艺配方优化的颠覆性半导体软件工具

基本信息

批准号：
1951245
负责人：
Meghali Chopra
金额：
$ 73.48万
依托单位：
SandBox Semiconductor Incorporated
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1951245&HistoricalAwards=false
关键词：
SBIR Phase II Disruptive Semiconductor

项目摘要

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is the enablement of next generation manufacturing technologies for semiconductor devices. The deployment of novel semiconductor devices such as 3D NAND, the latest device architecture developed for flash memory, is often stymied by the development of nanomanufacturing processes. The objective of this proposal is to build upon a recipe prediction platform to create a standalone software solution for semiconductor equipment and chip manufacturers to rapidly optimize their processes. In this research, the software speed will be increased, prediction capabilities will be extended to 3D structures, and model calibration will be automated to facilitate customer deployment. By accelerating the development of the processes used in device fabrication, this project reduces the cost of recipe development in the semiconductor industry and bringing next-generation technologies to fruition.This Small Business Innovation Research (SBIR) Phase II project will develop new advanced physical and statistical models to quickly and accurately predict outcomes for processes like plasma etch and deposition. In this research, 2D and 3D profile simulations will be algorithmically optimized for speed to give process engineers instantaneous results. Machine learning models will be used to enable process engineers to rapidly explore complex trade spaces. Sophisticated numerical algorithms will be developed to help engineers maximize process windows. Innovations in statistical and physics-based tools will permit process engineers to build and calibrate models themselves, enabling next-generation technologies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力是实现下一代半导体设备制造技术。新型半导体器件的部署，如3D NAND（为闪存开发的最新器件架构），经常受到纳米制造工艺发展的阻碍。本提案的目标是建立一个配方预测平台，为半导体设备和芯片制造商创建一个独立的软件解决方案，以快速优化其流程。在本研究中，软件速度将提高，预测能力将扩展到3D结构，模型校准将自动化，以方便客户部署。通过加速器件制造过程的开发，该项目降低了半导体行业配方开发的成本，并使下一代技术得以实现。这个小企业创新研究（SBIR）二期项目将开发新的先进物理和统计模型，以快速准确地预测等离子蚀刻和沉积等过程的结果。在这项研究中，2D和3D轮廓模拟将通过算法优化速度，为工艺工程师提供即时结果。机器学习模型将用于使流程工程师能够快速探索复杂的贸易空间。将开发复杂的数值算法来帮助工程师最大限度地提高工艺窗口。基于统计和物理的工具的创新将允许流程工程师自己构建和校准模型，从而实现下一代技术。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Creating new multistep etch and deposition processes with recycled etch data using SandBox Studio AI™

使用 SandBox Studio AI™ 使用回收的蚀刻数据创建新的多步蚀刻和沉积工艺

DOI：
10.1117/12.2614301
发表时间：
2022
期刊：
Advanced Etch Technology and Process Integration for Nanopatterning XI
影响因子：
0
作者：
Kearney, Kara;Medina, Leandro;Bonnecaze, Roger;Chopra, Meghali C.
通讯作者：
Chopra, Meghali C.

Fast etch recipe creation with automated model-based process optimization

通过基于模型的自动化工艺优化快速创建蚀刻配方

DOI：
10.1117/12.2583868
发表时间：
2021
期刊：
Advanced Etch Technology and Process Integration for Nanopatterning X
影响因子：
0
作者：
Ban, Yang;Kearney, Kara;Sundahl, Bryan;Medina, Leandro;Bonnecaze, Roger T.;Chopra, Meghali J.
通讯作者：
Chopra, Meghali J.

A method for achieving sub-2nm across-wafer uniformity performance

一种实现亚 2nm 跨晶圆均匀性性能的方法

DOI：
10.1117/12.2662423
发表时间：
2023
期刊：
Advanced Etch Technology and Process Integration for Nanopatterning
影响因子：
0
作者：
Ban, Yang;Medina, Leandro;Da Silva, Michael;Naranjo, Sebastian;Chopra, Meghali J.
通讯作者：
Chopra, Meghali J.

Automated, high throughput optimization of multistep and cyclic etch and deposition processes using SandBox Studio AI

使用 SandBox Studio AI 对多步骤和循环蚀刻和沉积过程进行自动化、高通量优化