公开(公告)号：US20240047248A1

公开(公告)日：2024-02-08

申请号：US18258497

申请日：2021-12-13

Applicant: Lam Research Corporation

Inventor： Dipongkar Talukder ,  Yan Zhang ,  Ye Feng ,  Jeffrey D. Bonde

IPC: H01L21/67 ,  H01L21/66

CPC classification number: H01L21/67253 ,  H01L22/12

Abstract: Various embodiments herein relate to systems and methods for adaptive model training. In some embodiments, a computer program product for adaptive model training is provided, the computer program product comprising a non-transitory computer readable medium on which is provided computer-executable instructions for: receiving, from a plurality of process chambers, ex situ data associated with wafers fabricated using the process chambers and in situ measurements, wherein a first machine learning model is used to predict the ex situ data using the in situ measurements; calculating a metric indicating an error associated with the first machine learning model; determining whether to update the first machine learning model; and generating a second machine learning model using the ex situ data and the in situ measurements.

公开(公告)号：US20240096713A1

公开(公告)日：2024-03-21

申请号：US18256665

申请日：2021-12-14

Applicant: Lam Research Corporation

Inventor： Yan Zhang ,  Ye Feng ,  Dipongkar Talukder ,  Jeffrey D. Bonde ,  Weng Foong Woo ,  Karthik Thimmavajjula ,  Jorge Luque

IPC: H01L21/66 ,  G06N20/00 ,  H01L21/67

CPC classification number: H01L22/20 ,  G06N20/00 ,  H01L21/67253

Abstract: Methods and systems for using a time-series of spectra to identify endpoint of a multi-step semiconductor fabrication processes such as multi-step deposition and multi-step etch processes. One method includes accessing a virtual carpet (e.g., a machine learning model) that is formed from a time-series of spectra for the multi-step processes collected during a training operation. During production, in-situ time-series of spectra are compared to the virtual carpet as part of end pointing of multi-step fabrication processes.