Applying Machine Learning Models on Metrology Data for Predicting Device Electrical Performance

• URL: http://arxiv.org/abs/2312.09462v1
• Date: Mon, 20 Nov 2023 10:55:09 GMT
• Title: Applying Machine Learning Models on Metrology Data for Predicting Device Electrical Performance
• Authors: Bappaditya Dey, Anh Tuan Ngo, Sara Sacchi, Victor Blanco, Philippe Leray, and Sandip Halder
• Abstract summary: Moore Law states that transistor density will double every two years, which is sustained until today due to continuous innovations. For any patterning scheme, the most important metric to evaluate the quality of printed patterns is EPE. Overlay errors can lead to fatal failures of IC devices such as short circuits or broken connections in terms of P2P electrical contacts.
• Score: 0.10051474951635875
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Moore Law states that transistor density will double every two years, which is sustained until today due to continuous multi-directional innovations, such as extreme ultraviolet lithography, novel patterning techniques etc., leading the semiconductor industry towards 3nm node and beyond. For any patterning scheme, the most important metric to evaluate the quality of printed patterns is EPE, with overlay being its largest contribution. Overlay errors can lead to fatal failures of IC devices such as short circuits or broken connections in terms of P2P electrical contacts. Therefore, it is essential to develop effective overlay analysis and control techniques to ensure good functionality of fabricated semiconductor devices. In this work we have used an imec N14 BEOL process flow using LELE patterning technique to print metal layers with minimum pitch of 48nm with 193i lithography. FF structures are decomposed into two mask layers (M1A and M1B) and then the LELE flow is carried out to make the final patterns. Since a single M1 layer is decomposed into two masks, control of overlay between the two masks is critical. The goal of this work is of two-fold as, (a) to quantify the impact of overlay on capacitance and (b) to see if we can predict the final capacitance measurements with selected machine learning models at an early stage. To do so, scatterometry spectra are collected on these electrical test structures at (a)post litho, (b)post TiN hardmask etch, and (c)post Cu plating and CMP. Critical Dimension and overlay measurements for line-space pattern are done with SEM post litho, post etch and post Cu CMP. Various machine learning models are applied to do the capacitance prediction with multiple metrology inputs at different steps of wafer processing. Finally, we demonstrate that by using appropriate machine learning models we are able to do better prediction of electrical results.

Related papers

• Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusability [12.425166883814153]
  Digital twin (DT) using machine learning (ML)-based modeling can be deployed for AM process monitoring and control. Melt pool is one of the most commonly observed physical phenomena for process monitoring. This paper proposes a knowledge transfer pipeline between different AM settings to enhance the reusability of AM DTs.
  arXiv  Detail & Related papers  (2024-09-19T13:54:01Z)
• COEFF-KANs: A Paradigm to Address the Electrolyte Field with KANs [5.759388420139191]
  We propose a novel method to automatically predict Coulombic Efficiency (CE) based on the composition of liquid electrolytes. We input the obtained electrolyte features into a Multi-layer Perceptron or Kolmogorov-Arnold Network to predict CE. Experimental results on a real-world dataset demonstrate that our method achieves SOTA for predicting CE compared to all baselines.
  arXiv  Detail & Related papers  (2024-07-24T14:45:25Z)
• Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning [116.75939193785143]
  Contrastive learning (CL) for Vision Transformers (ViTs) in image domains has achieved performance comparable to CL for traditional convolutional backbones. In 3D point cloud pretraining with ViTs, masked autoencoder (MAE) modeling remains dominant.
  arXiv  Detail & Related papers  (2024-07-08T12:28:56Z)
• Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillation [0.0]
  multimodal industrial anomaly detection (IAD) based on 3D point clouds and RGB images remains a work in progress. Existing quality control processes combine rapid in-line inspections, such as optical and infrared imaging with high-resolution but time-consuming near-line characterization techniques. We propose CMDIAD, a Cross-Modal Distillation framework for IAD to demonstrate the feasibility of a Multi-modal Training, Few-modal Inference pipeline.
  arXiv  Detail & Related papers  (2024-05-22T12:08:56Z)
• DB-LLM: Accurate Dual-Binarization for Efficient LLMs [83.70686728471547]
  Large language models (LLMs) have significantly advanced the field of natural language processing. Existing ultra-low-bit quantization always causes severe accuracy drops. We propose a novel Dual-Binarization method for LLMs, namely DB-LLM.
  arXiv  Detail & Related papers  (2024-02-19T09:04:30Z)
• SEMI-DiffusionInst: A Diffusion Model Based Approach for Semiconductor Defect Classification and Segmentation [0.11999555634662631]
  This work is the first demonstration to accurately detect and precisely segment semiconductor defect patterns by using a diffusion model. Our proposed approach outperforms previous work on overall mAP and performs comparatively better or as per for almost all defect classes.
  arXiv  Detail & Related papers  (2023-07-17T17:53:36Z)
• Pre-training Language Model as a Multi-perspective Course Learner [103.17674402415582]
  This study proposes a multi-perspective course learning (MCL) method for sample-efficient pre-training. In this study, three self-supervision courses are designed to alleviate inherent flaws of "tug-of-war" dynamics. Our method significantly improves ELECTRA's average performance by 2.8% and 3.2% absolute points respectively on GLUE and SQuAD 2.0 benchmarks.
  arXiv  Detail & Related papers  (2023-05-06T09:02:10Z)
• An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design [55.62660894625669]
  Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. Recent developments in machine learning have provided alternative solutions in replacing the time-consuming lithography simulations with deep neural networks. We propose a litho-aware data augmentation framework to resolve the dilemma of limited data and improve the machine learning model performance.
  arXiv  Detail & Related papers  (2022-10-27T20:53:39Z)
• Self-learning locally-optimal hypertuning using maximum entropy, and comparison of machine learning approaches for estimating fatigue life in composite materials [0.0]
  We develop an ML nearest-neighbors-alike algorithm based on the principle of maximum entropy to predict fatigue damage. The predictions achieve a good level of accuracy, similar to other ML algorithms.
  arXiv  Detail & Related papers  (2022-10-19T12:20:07Z)
• Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT [82.33080550378068]
  The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue. This paper studies the FL technology applications to manage IIoT equipment data in wireless network environments.
  arXiv  Detail & Related papers  (2022-02-03T07:12:36Z)
• Efficient pre-training objectives for Transformers [84.64393460397471]
  We study several efficient pre-training objectives for Transformers-based models. We prove that eliminating the MASK token and considering the whole output during the loss are essential choices to improve performance.
  arXiv  Detail & Related papers  (2021-04-20T00:09:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.