Browsing by Author "Hsiao, Chia-Chi"

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Manipulation System Design for Industrial Robot Manipulators Based on Tablet PC
(2015-01-01)
Su, Yu-Hsuan
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Hsiao, Chia-Chi
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Young, Kuu-Young
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電機工程學系
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Department of Electrical and Computer Engineering
Along with the progress of robot technology, nowadays industrial robot manipulators are applied for more diverse and complex tasks. Consequently, the conventional manipulative devices, such as the teaching pendant and keyboard, become unsuitable for task teaching or planning. Motivated by it, in this paper, we propose a new type of manipulation system for industrial robot manipulators based on tablet PC. With wireless communication, the proposed system provides flexibility for remote manipulation. In addition, a graphical interface and several assistive tools are equipped for viewing and maneuvering tasks during execution, allowing the operator to conduct the task in a more intuitive way. We conducted experiments using both the proposed manipulation system and conventional manipulative device.
On Distinguishing Process Corners for Yield Enhancement in Memory Compiler Generated SRAM
(2009)
Hsiao, Chia-Chi
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Chen, Hung-Ming
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
As the technology scales down to nanometer, the yield degradation caused by inter-die variations is getting worse. Using adaptive body bias is an effective method to mitigate the yield degradation (especially for memory compiler generated SRAMs), however we need to know a die having high threshold voltage or low threshold voltage (also called process corner) in order to use this technique. Unfortunately, it is hard to detect the process corners when PMOS and NMOS variations are uncorrelated. In this paper, we propose some improved circuits of delay monitor and leakage monitor for both PMOS and NMOS process corner detection, which are uncorrelated in inter-die variations. The experimental results show that our circuits can clearly distinguish each process corner of PMOS and NMOS, thus improve the yield by adopting correct body bias.