首页|Researcher from National University of Singapore Publishes Findings in Machine L earning (Efficient machine learning-assisted failure analysis method for circuit -level defect prediction)
Researcher from National University of Singapore Publishes Findings in Machine L earning (Efficient machine learning-assisted failure analysis method for circuit -level defect prediction)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting out of National Unive rsity of Singapore by NewsRx editors, research stated, “Integral to the success of transistor advancements is the accurate use of failure analysis (FA) which be nefits in fine-tuning and optimization of the fabrication processes. However, th e chip makers face several FA challenges as device sizes, structure, and materia l complexities scale dramatically.” The news journalists obtained a quote from the research from National University of Singapore: “To sustain manufacturability, one can accelerate defect identifi cation at all steps of the chip processing and design. On the other hand, as tec hnologies scale below the nanometer nodes, devices are more sensitive to unavoid able process-induced variability. Therefore, metallic defects and process-induce d variability need to be treated concurrently in the context of chip scaling, wh ile failure diagnostic methods to decouple the effects should be developed. Inde ed, the locating a defective component from thousands of circuits in a microchip in the presence of variability is a tedious task. This work shows how the SPICE circuit simulations coupled with machine learning based-physical modeling shoul d be effectively used to tackle such a problem for a 6T-SRAM bit cell. An automa tic bridge defect recognition system for such a circuit is devised by training a predictive model on simulation data. For feature descriptors of the model, the symmetry of the circuit and a fundamental material property are leveraged: metal s (semiconductors) have a positive (negative) temperature coefficient of resista nce up to a certain voltage range.”
National University of SingaporeCyborg sEmerging TechnologiesMachine Learning
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