An Analysis Mechanism of Signal Quality Simulation Based on Bayesian Network Classification

• Main Authors: CHIANG,YUN-CHUNG, 江允中
• Other Authors: JUANG,TONG-YING
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/05747434962231793323

碩士 === 國立臺北大學 === 多媒體與網路科技產業碩士專班 === 104 === In today's industrial computer ecosystem, analysis and integration of abnormal signal problems play important role but yet to offer handy tool to tackle with. As a result, members of R&D designer have to take a long time, from different production samples to identify cause of failure. Then, re-production samples all over to verify the effect. This approach not only inefficient, wasting lots of analysis time, rising cost in sampling, but also leads customers to losing confidence and patience. Therefore, how to allow R&D designer to master the key factors in no time and devote in the core of the problem so as to shorten the analysis time, becomes focus of current research of signal analysis. The quality and compatibility of the signal test depend on whether it can pass the eye diagram specifications. Professional simulation software HSPICE, and Bayesian network classification have been chosen as models to analysis with Existing analog signal sample that has been done with the actual output of the analysis record, and combined with quality of verification data (eye diagram). In addition, this thesis uses Bayesian network classification, critical characteristic factor (PCB impedance, Chipset's input voltage, signal routing way, routing length of PCB layers stacked configuration), over the integration of individual probability reason the problem. Those eigenvalues then being included in conditional Bayesian probability processing. Besides, define and list out all the permutations and combinations of variables. For each combination of classification, based on Bayesian classification, calculate the corresponding probability of occurrence, and elect the highest probability as the combination of conditions classification. Finally, verification going through a combination of the probability of existing conditions is used and fitting with output of original sample signal simulation. It may be proved that the success ratio of signal quality could be significantly improved in this way. Thus help R&D designer in grasping the regulation of key factors to make the appropriate choice at the very start. And ensure that production of samples can meet the requirements of the design specification. Meanwhile, reduce the time cost of reproduction and improve the success of the hit rate. It will be the major contribution of this analysis method .