多观测下基于模型诊断和修复方法研究

基于模型诊断方法已受到工业界和学术界的高度重视，但随着集成电路规模呈摩尔规律的爆炸式增长，若此领域没有重大技术突破，其将成为制约集成电路高速发展的一个重要障碍。.现有模型诊断方法未能充分利用多观测间逻辑关系等诸多信息，本项目拟挖掘多观测间逻辑关系等信息，研究逻辑关系指导下高效的模型诊断求解方法和元件修复方法。提取多观测间蕴含和支配等逻辑关系，融合逻辑关系提升模型诊断方法可求解规模和求解效率；给出元件功能和拓扑等关系指导的探测点选择方法，提高不可区分故障对的识别率；结合元件、诊断解和观测间逻辑关系优化元件健康态计算方法，避免健康态求解偏差较大的问题；在此基础上，给出结合元件健康态和诊断聚类的批量元件修复方法，有效提高元件修复准确性和降低元件修复成本。.项目的预期成果将丰富和拓展多观测下模型诊断研究的理论与方法，有效解决多观测下诊断面临的挑战性技术难题。

Model-based diagnosis has attracted attention in the fields of academics and industry. Due to the explosive growth of the scale of integrated circuits, which makes an obstacle in the development of integrated circuits, technological breakthrough in this field is extremely urgent.. Existing model-based diagnosis methods fail to take advantage of the information provided by the logical relation among multiple observations. This project intends to mine the logical relation and other information among multiple observations and study the model-based diagnosis problem and methods for component repairing guided by logical relation. Moreover, extracting logical relation such as implication and domination among multiple observations, and integrating logical relation to improve the scale and efficiency of model-based diagnosis problem will be studied. To improve the recognition rate of indistinguishable fault pairs, the method for selecting the detection points guided by component functions and topology will be proposed; In addition, the logical relation among components, diagnosis, and observations optimizes the method for computing the health state, which avoids the problem of large deviations in the health state. Based on this, a method for repairing batch components combining health state and diagnostic clustering is presented to effectively improve the accuracy and reduce the cost.. The achievement of this project is to enrich and expand the theory and methods of model-based diagnosis problem under multiple observations, and effectively solve the challenging technical problems.