Institution
Amkor Technology
Company•Tempe, Arizona, United States•
About: Amkor Technology is a company organization based out in Tempe, Arizona, United States. It is known for research contribution in the topics: Semiconductor package & Substrate (printing). The organization has 1069 authors who have published 1106 publications receiving 26778 citations. The organization is also known as: Amkor & Amkor Technology, Inc..
Topics: Semiconductor package, Substrate (printing), Die (integrated circuit), Layer (electronics), Flip chip
Papers published on a yearly basis
Papers
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01 Oct 2006
TL;DR: NiP used for NoC is designed and analyzed regarding of signal integrity and power integrity and the measured results of the NiP show perfect communications between NoCs.
Abstract: SiP (system-in-package) and SoC (system-on-chip) are familiar to us. In this paper, we firstly define advanced concepts of NoC (network-on-chip) and NiP (network-in-package). Design and implementation of NoC are explained and then, NiP used for NoC is designed and analyzed regarding of signal integrity and power integrity. The low-power packet-switched NoC with hierarchical star topology is designed and implemented for high-performance SoC platform. An NiP integrating four NoCs is fabricated in a 676-BGA-type package for large and scalable systems and the measured results of the NiP show perfect communications between NoCs
2 citations
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01 Jan 2014TL;DR: In terms of processing, devices using Cu Pillar are most commonly assembled using thermo-compression bonding due to sizable challenges in extending the conventional mass reflow solution, typical for solder bumps.
Abstract: Flip chip interconnection design and process capability has been extended with the use of copper pillar, specially for finer pitches, beyond what was possible with area array and standard C4 reflow and solder bumps. Many trends in device packaging are fueling this trend, specially the needs for thin packaging, increased function integration, challenging thermal, mechanical and physical specifications. In terms of processing, devices using Cu Pillar are most commonly assembled using thermo-compression bonding due to sizable challenges in extending the conventional mass reflow solution, typical for solder bumps. TC is specially recommended for handling very thin die in sparse or peripheral bump layouts, which are a predominant share of all devices converting to Cu pillar, where a bonding head is used to both hold the die flat and in true alignment with the substrate while supplying the thermal energy necessary to complete the interconnection. This process most often also requires the use of a non-conductive...
2 citations
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26 May 2009TL;DR: In this article, a laptop computer was disassembled and several thermocouples were attached to the die, heat sink, substrate, motherboard, thermal interface pad, and underfill materials.
Abstract: A laptop computer was disassembled, and several thermocouples were attached to the die, heat sink, substrate, and motherboard. The laptop was then re-assembled and run under various conditions to measure the effects on temperature distribution. The assembly was then deconstructed and samples were extracted for material property measurements. Elastic modulus and thermal expansivity were measured for the heat sink, substrate, motherboard, thermal interface pad, and underfill materials. Typical temperature rise of the IGP die above ambient was 40C. Video use increased the temperature by 5C to 10C. Wrapping the laptop to constrict airflow increased the temperature by 15C. Hence, the operating temperature range is approximately 55C to 80C. The substrate and motherboard are hotter on the side facing the CPU. There are gradients of up to 20C in the structure (difference between hottest and coolest regions). The initial temperature change rate during a power cycle is 5C/sec for the IGP die.
2 citations
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05 Jul 2012TL;DR: In this paper, a Random Network Model (RNM) was developed to capture the near-percolation transport in these particle-filled systems, which can take into account the interparticle interactions and random size distributions.
Abstract: Thermal Interface Materials (TIMs) are particulate composite materials widely used in the microelectronics industry to reduce the thermal resistance between the device and the heat sink Predictive modeling using fundamental physical principles is critical to developing new TIMs, since it can be used to quantify the effect of polydisersivity, volume fraction and arrangements on the effective thermal conductivity A Random Network Model (RNM) that can efficiently capture the near-percolation transport in these particle-filled systems was developed by the authors, which can take into account the inter-particle interactions and random size distributions The accuracy of the RNM is dependent on the parameters inherent in analytical description of thermal transport between two spherical particles, and their numerical approximation into a network model In the present study, COMSOL™ was used to conduct polydispersivity studies that enabled the refinement of the analytical model Comparing RNM results with FE results, the relation of a critical parameter with the polydispersivity and the volume fraction of the fillers in TIMs was found that provides a more accurate prediction of the effective thermal conductivity of the particulate TIMs using RNM
2 citations
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01 Oct 2019TL;DR: Using Low-Density Fan-Out (LDFO) packaging technology, a radio frequency (RF) microelectromechanical systems (MEMS) tunable capacitor array composed of electrostatically actuated beams on...
Abstract: Using Low-Density Fan-Out (LDFO) packaging technology, a radio frequency (RF) microelectromechanical systems (MEMS) tunable capacitor array composed of electrostatically actuated beams on ...
2 citations
Authors
Showing all 1070 results
Name | H-index | Papers | Citations |
---|---|---|---|
Thomas P. Glenn | 48 | 130 | 6676 |
Dong-Hoon Lee | 48 | 762 | 23162 |
Joungho Kim | 40 | 579 | 7365 |
Steven Webster | 34 | 83 | 3322 |
Young Bae Park | 33 | 216 | 4325 |
Roy Dale Hollaway | 28 | 53 | 2324 |
Ronald Patrick Huemoeller | 26 | 91 | 2385 |
Robert Francis Darveaux | 23 | 70 | 1881 |
MinJae Lee | 23 | 99 | 3083 |
Il Kwon Shim | 21 | 41 | 1403 |
Vincent DiCaprio | 20 | 27 | 1973 |
Sukianto Rusli | 19 | 44 | 1308 |
Glenn A. Rinne | 19 | 34 | 898 |
Ahmer Syed | 18 | 55 | 1192 |
David Jon Hiner | 18 | 54 | 1173 |