A Distributed-Ledger, Edge-Computing Architecture for Automation and Computer Integration in Semiconductor Manufacturing

2020 ◽  
pp. 13-29
Author(s):  
Da-Yin Liao
Keyword(s):  
Semiconductor Manufacturing ◽  
Manufacturing Systems ◽  
Edge Computing ◽  
Legacy Systems ◽  
Software Framework ◽  
Computer Integration ◽  
Computing Architecture ◽  
Disruptive Technologies ◽  
Distributed Ledger ◽  
Operational Structure

Contemporary 300mm semiconductor manufacturing systems have highly automated and digitalized cyber-physical integration. They suffer from the profound problems of integrating large, centralized legacy systems with small islands of automation. With the recent advances in disruptive technologies, semiconductor manufacturing has faced dramatic pressures to reengineer its automation and computer integrated systems. This paper proposes a Distributed-Ledger, Edge-Computing Architecture (DLECA) for automation and computer integration in semiconductor manufacturing. Based on distributed ledger and edge computing technologies, DLECA establishes a decentralized software framework where manufacturing data are stored in distributed ledgers and processed locally by executing smart contracts at the edge nodes. We adopt an important topic of automation and computer integration for semiconductor research &development (R&D) operations as the study vehicle to illustrate the operational structure and functionality, applications, and feasibility of the proposed DLECA software framework.

scholarly journals A Proximal Algorithm for Fork-Choice in Distributed Ledger Technology for Context-Based Clustering on Edge Computing

2020 ◽  
Vol 2 (1) ◽  
pp. 92
Author(s):  
Rahim Rahmani ◽  
Ramin Firouzi ◽  
Sachiko Lim ◽  
Mahbub Alam
Keyword(s):  
Large Scale ◽  
Cluster Head ◽  
Edge Computing ◽  
Computing Technology ◽  
Proximal Algorithm ◽  
Data Intensive ◽  
Distributed Ledger ◽  
Smart Contract ◽  
Distributed Ledger Technology ◽  
Iot Devices

The major challenges of operating data-intensive of Distributed Ledger Technology (DLT) are (1) to reach consensus on the main chain as a set of validators cast public votes to decide on which blocks to finalize and (2) scalability on how to increase the number of chains which will be running in parallel. In this paper, we introduce a new proximal algorithm that scales DLT in a large-scale Internet of Things (IoT) devices network. We discuss how the algorithm benefits the integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on proximity context information. A cluster head is used to bridge the IoT devices with the DLT network where a smart contract is deployed. In this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate on our mechanism and discuss issues that should be considered and implemented when using the proposed algorithm, we even show how it behaves with varying parameters like latency or when clustering.

On-the-Fly Substrate Eccentricity Recognition for Robotized Manufacturing Systems

2005 ◽  
Vol 127 (1) ◽  
pp. 206-216 ◽  
Author(s):  
Martin Hosek ◽  
Jan Prochazka
Keyword(s):  
Optical Sensors ◽  
Semiconductor Manufacturing ◽  
Manufacturing Systems ◽  
Robotic Manipulator ◽  
End Effector ◽  
Transfer Path ◽  
The Difference ◽  
Manufacturing Applications ◽  
Actual Location

This paper describes a method for on-the-fly determination of eccentricity of a circular substrate, such as a silicon wafer in semiconductor manufacturing applications, carried by a robotic manipulator, where eccentricity refers to the difference between the actual location of the center of the substrate and its desired position on the end-effector of the robotic manipulator. The method utilizes a pair of external optical sensors located along the substrate transfer path. When moving a substrate along the transfer path, the robotic manipulator captures the positions and velocities of the end-effector at which the edges of the substrate are detected by the sensors. These data along with the expected radius of the substrate and the coordinates of the sensors are used to determine the eccentricity of the substrate. This information can be used by the robotic manipulator to compensate for eccentricity of the substrate when performing a place operation, resulting in the substrate being placed centered regardless of the amount and direction of the initial eccentricity. The method can also be employed to detect a defect, such as breakage, of a circular substrate and report an error condition which can abort or otherwise adjust operation of the robotic manipulator.

A novel edge computing architecture for intelligent coal mining system

2022 ◽  
Author(s):  
Zhe Bing ◽  
Xing Wang ◽  
Zhenliang Dong ◽  
Luobing Dong ◽  
Tao He
Keyword(s):  
Coal Mining ◽  
Edge Computing ◽  
Mining System ◽  
Computing Architecture
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