Knowledge Engineering for Semiconductor Yield Analysis: Tool Application and Fault Symptom Identification

Quick yield enhancement is one of the critical aspects of engineering chain management. In the sub-wavelength era, cycle time requirement becomes more stringent because capital investment is sky rocketing while market demands change more rapidly and the manufacturing process, equipments and operations become more complicated than before. Fast yield ramping is founded on effective management of knowledge intensive yield analysis. Semiconductor fabs adopt engineering data analysis (EDA) platforms provided by vendors as basis to develop fab specific analysis function tool suite with domain expert knowledge to assist engineers in yield analysis. What further differentiate the effectiveness of yield analysis among fabs are the yield analysis procedures (YAPs) that engineers combine their knowledge with the applications of the EDA tools for problem solving. AP consists of three layers: Triggered by a yield analysis event, an engineer generates analysis purposes and the corresponding plan (Purpose layer), and then selects and applies appropriate EDA tools in sequence (Tool layer) and identify fault symptoms of EDA tool output to perform analysis according to the purpose plan to accomplish his/her goal. EDA tool provision is by the third layer, EDA platform. In spite of rich tool suites provided by current EDA platforms, YAP knowledge is largely in engineers’ brains or in disparate documentation; they have not been systematically extracted neither stored in EDA system. In this thesis, we use symptom identification as the conveyor problem, the research focuses on mechanism designs to extract EDA tool application procedure and fault symptom identification knowledge for sharing and reusing to achieve more effective yield analysis. Specific design challenges are as follows: (1) How to provide applicable EDA tools by engineers’ specified analysis purposes? (2) How to extract implicit engineers’ preferences of EDA tool application procedures from currently available data? (3) How to capture and store perceived graphic symptoms by engineers and store them? hree mechanisms are designed to conquer these challenges respectively as follow:. Establishment of linkage between analysis purpose and EDA tool: nified Resource Model based EDA tool description. URM is a semantic network consisting of node types, nodes and node links. In the context of semiconductor manufacturing, nodes correspond to data names like process and equipment, etc, while the nodes which have the same property are classified into a node type. Node link corresponds to the relation between two nodes. The linkage between purposes and EDA tools is constructed by matching their semantic meaning in URM terms.. Extraction of EDA tool procedure and purpose knowledge from empirical data: arkov Chain based procedure knowledge extraction algorithm (MCPKE). By modeling an EDA tool as a state, the sequential tool applications as state transitions and the state transition probabilities as engineers’ preferences, the analysis procedures of applying EDA tools are modeled as a Markov Chain, MCPKE algorithm has been developed to extract the process knowledge from empirical event log data. In conjunction with the URM-based linkage, EDA procedure knowledge is then exploited to extract engineers’ purpose plans that drive the application procedures.. Symptom identification knowledge extraction through extra effort free interface: raphic-to-text implicit symptom capturer (GSC). To capture engineers’ observed fault symptoms through the graphic user interfaces they use daily without requiring extra reporting efforts, a graphic symptom capturer technique is designed that enables an engineer to directly select the symptom pattern he/she perceives over the graphic charts. The graphic symptom pattern is then translated into text descriptions by a design of software interpreter for knowledge storage and sharing. To reveal the knowledge extraction mechanisms which are achievable, we implement a Service Oriented Architecture (SOA) based EDA platform and integrate the mechanisms on it. The four main values of the platform are enhanced by the knowledge extraction mechanisms as follow:. Reuse and sharing of EDA tool procedure knowledge without extra documentation efforts,. Reduction of cycle time from specified purpose to identified EDA tool via URM-based linkage, . Extract EDA tool application procedure knowledge via MCPKE algorithm and URM, and enhance engineers’ purpose plan knowledge extraction,. Automatically translate graphic symptoms which engineers observed to symptom texts by GSC.