Enabling Requirements for High Volume Thermo compression Bonding

2015 ◽  
Vol 2015 (DPC) ◽  
pp. 000995-001015
Author(s):  
Tom Strothmann
Keyword(s):  
Process Control ◽  
Cost Reduction ◽  
State Of The Art ◽  
High Volume ◽  
Manufacturing Technology ◽  
Data Logging ◽  
Key Factors ◽  
Review State ◽  
Memory Applications

The potential of Thermo compression Bonding (TCB) has been widely discussed for several years, but it has not previously achieved widespread production use. TCB has now begun the transition to an accepted high volume manufacturing technology driven primarily by the memory market, but with wider adoption close for non-memory applications. Several key factors have enabled this transition, including advanced TCB equipment with higher UPH for cost reduction and advanced methods of inline process control. The unique requirements of TCB demand absolute process control, simultaneous data logging capability for multiple key factors in the process and portability of the process between tools. This introduces a level of sophistication that has not previously been required for BE assembly processes. This presentation will review state of the art TCB technology and the fundamental equipment requirements to support the transition to HVM.

scholarly journals Mining Streaming and Temporal Data: from Representation to Knowledge

2018 ◽  
Author(s):  
Xiangliang Zhang
Keyword(s):  
Big Data ◽  
State Of The Art ◽  
Complex Structure ◽  
High Volume ◽  
Representation Learning ◽  
Irrelevant Information ◽  
Temporal Data ◽  
Computationally Efficient ◽  
Financial Applications ◽  
Review State

In this big-data era, vast amount of continuously arriving data can be found in various fields, such as sensor networks, network management, web and financial applications. To process such data, algorithms are usually challenged by its complex structure and high volume. Representation learning facilitates the data operation by providing a condensed description of patterns underlying the data. Knowledge discovery based on the new representations will then be computationally efficient, and to certain extent be more effective due to the removal of noise and irrelevant information in the step of representation learning. In this paper, we will briefly review state-of-the-art techniques for extracting representation and discovering knowledge from streaming and temporal data, and demonstrate their performance at addressing several real application problems.

scholarly journals A secure and highly efficient first-order masking scheme for AES linear operations

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Jingdian Ming ◽  
Yongbin Zhou ◽  
Huizhong Li ◽  
Qian Zhang
Keyword(s):  
Cost Reduction ◽  
Provable Security ◽  
State Of The Art ◽  
Side Channel ◽  
First Order ◽  
Highly Efficient ◽  
Non Linear ◽  
Device Independence ◽  
Practical Implications ◽  
Provably Secure

AbstractDue to its provable security and remarkable device-independence, masking has been widely accepted as a noteworthy algorithmic-level countermeasure against side-channel attacks. However, relatively high cost of masking severely limits its applicability. Considering the high tackling complexity of non-linear operations, most masked AES implementations focus on the security and cost reduction of masked S-boxes. In this paper, we focus on linear operations, which seems to be underestimated, on the contrary. Specifically, we discover some security flaws and redundant processes in popular first-order masked AES linear operations, and pinpoint the underlying root causes. Then we propose a provably secure and highly efficient masking scheme for AES linear operations. In order to show its practical implications, we replace the linear operations of state-of-the-art first-order AES masking schemes with our proposal, while keeping their original non-linear operations unchanged. We implement four newly combined masking schemes on an Intel Core i7-4790 CPU, and the results show they are roughly 20% faster than those original ones. Then we select one masked implementation named RSMv2 due to its popularity, and investigate its security and efficiency on an AVR ATMega163 processor and four different FPGA devices. The results show that no exploitable first-order side-channel leakages are detected. Moreover, compared with original masked AES implementations, our combined approach is nearly 25% faster on the AVR processor, and at least 70% more efficient on four FPGA devices.

Man-machine interaction in process control. A state-of-the-art report

1987 ◽  
Vol 18 (3) ◽  
pp. 253
Author(s):  
R.N. Pikaar
Keyword(s):  
Process Control ◽  
State Of The Art ◽  
Machine Interaction

scholarly journals Inlet and Nozzle Technology for 21st Century Fighter Aircraft

1996 ◽  
Author(s):  
Marvin C. Gridley ◽  
Steven H. Walker
Keyword(s):  
21St Century ◽  
Cost Reduction ◽  
State Of The Art ◽  
Aerodynamic Performance ◽  
Innovative Technology ◽  
Fighter Aircraft ◽  
Current State ◽  
Aircraft System ◽  
Year 2000 ◽  
System Volume

The focus of propulsion integration technology in the 21st century will be economy. USAF inlet and nozzle technology goals translate into 50% weight reduction and 25% acquisition cost reduction metrics for new aircraft system. Innovative technology to enable these reductions over current state-of-the-art systems in weight and cost is required. For inlet systems, compact diffusers that reduce system volume by 50% will demand fewer parts and improved aerodynamic performance. Exhaust systems will be fixed with fewer parts, requiring a technology like fluidics, for example, to provide area control and thrust vectoring capabilities. Cooperative programs for both inlet and nozzle systems are in place to insure that technologies required to meet weight and cost reduction goals are matured by the year 2000.

Thermal Laser Separation – A Novel Dicing Technology Fulfilling the Demands of Volume Manufacturing of 4H-SiC Devices

2015 ◽  
Vol 821-823 ◽  
pp. 528-532 ◽  
Author(s):  
Dirk Lewke ◽  
Karl Otto Dohnke ◽  
Hans Ulrich Zühlke ◽  
Mercedes Cerezuela Barret ◽  
Martin Schellenberger ◽  
...  
Keyword(s):  
Experimental Data ◽  
Process Control ◽  
Tool Wear ◽  
Large Scale ◽  
State Of The Art ◽  
Scale Production ◽  
High Quality ◽  
Large Scale Production ◽  
Laser Separation

One challenge for volume manufacturing of 4H-SiC devices is the state-of-the-art wafer dicing technology – the mechanical blade dicing which suffers from high tool wear and low feed rates. In this paper we discuss Thermal Laser Separation (TLS) as a novel dicing technology for large scale production of SiC devices. We compare the latest TLS experimental data resulting from fully processed 4H-SiC wafers with results obtained by mechanical dicing technology. Especially typical product relevant features like process control monitoring (PCM) structures and backside metallization, quality of diced SiC-devices as well as productivity are considered. It could be shown that with feed rates up to two orders of magnitude higher than state-of-the-art, no tool wear and high quality of diced chips, TLS has a very promising potential to fulfill the demands of volume manufacturing of 4H-SiC devices.

High-volume manufacturing device overlay process control

2017 ◽  
Author(s):  
Honggoo Lee ◽  
Sangjun Han ◽  
Jaeson Woo ◽  
DongYoung Lee ◽  
ChangRock Song ◽  
...  
Keyword(s):  
Process Control ◽  
High Volume

Towards Update-Efficient and Parallel-Friendly Content-Based Indexing Scheme in Cloud Computing

2018 ◽  
Vol 12 (02) ◽  
pp. 191-213
Author(s):  
Nan Zhu ◽  
Yangdi Lu ◽  
Wenbo He ◽  
Hua Yu ◽  
Jike Ge
Keyword(s):  
Cloud Computing ◽  
State Of The Art ◽  
High Volume ◽  
Data Partitioning ◽  
The State ◽  
Computing Systems ◽  
Indexing System ◽  
Indexing Method ◽  
Concurrent User ◽  
Art Research

The sheer volume of contents generated by today’s Internet services is stored in the cloud. The effective indexing method is important to provide the content to users on demand. The indexing method associating the user-generated metadata with the content is vulnerable to the inaccuracy caused by the low quality of the metadata. While the content-based indexing does not depend on the error-prone metadata, the state-of-the-art research focuses on developing descriptive features and misses the system-oriented considerations when incorporating these features into the practical cloud computing systems. We propose an Update-Efficient and Parallel-Friendly content-based indexing system, called Partitioned Hash Forest (PHF). The PHF system incorporates the state-of-the-art content-based indexing models and multiple system-oriented optimizations. PHF contains an approximate content-based index and leverages the hierarchical memory system to support the high volume of updates. Additionally, the content-aware data partitioning and lock-free concurrency management module enable the parallel processing of the concurrent user requests. We evaluate PHF in terms of indexing accuracy and system efficiency by comparing it with the state-of-the-art content-based indexing algorithm and its variances. We achieve the significantly better accuracy with less resource consumption, around 37% faster in update processing and up to 2.5[Formula: see text] throughput speedup in a multi-core platform comparing to other parallel-friendly designs.

scholarly journals Artificial intelligence maturity model: a systematic literature review

2021 ◽  
Vol 7 ◽  
pp. e661
Author(s):  
Raghad Baker Sadiq ◽  
Nurhizam Safie ◽  
Abdul Hadi Abd Rahman ◽  
Shidrokh Goudarzi
Keyword(s):  
Artificial Intelligence ◽  
Systematic Approach ◽  
State Of The Art ◽  
Model Development ◽  
Significant Proportion ◽  
Maturity Model ◽  
Methodological Issues ◽  
Maturity Models ◽  
Essential Contribution ◽  
Review State

Organizations in various industries have widely developed the artificial intelligence (AI) maturity model as a systematic approach. This study aims to review state-of-the-art studies related to AI maturity models systematically. It allows a deeper understanding of the methodological issues relevant to maturity models, especially in terms of the objectives, methods employed to develop and validate the models, and the scope and characteristics of maturity model development. Our analysis reveals that most works concentrate on developing maturity models with or without their empirical validation. It shows that the most significant proportion of models were designed for specific domains and purposes. Maturity model development typically uses a bottom-up design approach, and most of the models have a descriptive characteristic. Besides that, maturity grid and continuous representation with five levels are currently trending in maturity model development. Six out of 13 studies (46%) on AI maturity pertain to assess the technology aspect, even in specific domains. It confirms that organizations still require an improvement in their AI capability and in strengthening AI maturity. This review provides an essential contribution to the evolution of organizations using AI to explain the concepts, approaches, and elements of maturity models.

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