Advanced DBC - Highly Reliable and Conductive Copper Ceramic Substrates

2016 ◽  
Vol 2016 (CICMT) ◽  
pp. 000073-000078
Author(s):  
Paul Gundel ◽  
Anton Miric ◽  
Kai Herbst ◽  
Melanie Bawohl ◽  
Jessica Reitz ◽  
...  

Abstract So far Direct Bonded Copper (DBC) substrates have been the standard for power electronics. They provide excellent electrical and thermal conductivity at low cost. Weaknesses of DBC technology are the inevitable warpage and the relatively low reliability under thermal cycling. The low reliability poses a significant hurdle in particular for automotive applications with high lifetime requirements. Thick Print Copper (TPC) substrates with low warpage and excellent reliability overcome these weaknesses, but also provide a reduced conductivity at a higher cost. We present two thick-film/DBC hybrid technologies which combine the best properties of DBC and TPC: excellent conductivity, low cost, reduced warpage and excellent reliability.

1989 ◽  
Vol 154 ◽  
Author(s):  
Alistair D. Westwood ◽  
Michael R. Notis

AbstractMicrochemical and microstructural study has been carried out on the tungsten-aluminum nitride (W-AlN) thick film metallization interface. A reaction has been found to occur with the formation of precipitates at AlN grain boundaries and also within the AlN grains; a thin crystalline grain boundary film was also observed. The interface morphology and chemistry is compared to the molybdenum/manganese-aluminum nitride (MoMn-AlN) interface. The effects of morphology and microchemical variations upon thermal conductivity are discussed.


2016 ◽  
Vol 2016 (CICMT) ◽  
pp. 000024-000031
Author(s):  
Thomas Maeder ◽  
Caroline Jacq ◽  
Stefane Caseiro ◽  
Peter Ryser

Abstract Miniature ceramic cantilevers have been successfully applied to the fabrication of simple and low-cost piezoresistive thick-film force-sensing cells, using different thick-film and LTCC (low-temperature co-fired ceramic) substrates. The availability of thin substrates for some materials allows much improved sensitivity compared to classical thick-film technology, with LTCC also featuring rather low substrate elastic modulus and fine structurability. However, practical applicability may be hindered by processing difficulties, such as printing and handling very thin fired substrates, or, in the case of co-fired tapes, warpage during firing. Also, signal drift is observed with some devices. In this work, we show that most of the previously-observed signal drift in some LTCC sensors is not due to self-heating, and therefore stems from defects such as micro-cracks within the ceramic cantilevers or plastic deformations in internal conductors. In a second step, we explore manufacturability of thick-film cantilevers on very thin substrates, and show that it is possible to print a single-sided design on substrates with thickness as low as 45 μm, although a lower limit of ~100 μm, depending on substrate material, is more practical.


2019 ◽  
Vol 1309 ◽  
pp. 012016
Author(s):  
A D Kurilov ◽  
V V Belyaev ◽  
K D Nessemon ◽  
E D Besprozvannyi ◽  
A O Osin ◽  
...  

Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.


Solar Energy ◽  
2014 ◽  
Vol 108 ◽  
pp. 370-376 ◽  
Author(s):  
Wajiha Shireen ◽  
Adarsh Nagarajan ◽  
Sonal Patel ◽  
Radhakrishna Kotti ◽  
Preetham Goli

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