Abstract :
In an IC process line, after a process is finished, the process control module or monitor (PCM) is tested and the data are examined so that the status of the process quality is known. In the case of a process failure, the root cause of the failure must be analyzed, and relevant actions must be taken to correct it. In this paper, we describe a novel way of diagnosing process failures. First, the tested PCM parameters, which are correlated to each other, are analyzed and transformed to a new set of independent parameters using principal component analysis (PCA). In the second step, the most important eigenvectors from PCA calculation are identified, and the causes of the process failures can therefore be extracted. Furthermore, using the PCA eigenvectors as a coordinate base, the state space of a process can be constructed. As a result, the process states from different lots of wafers can be compared; thus, it is possible to trace the IC processes, or even to predict a possible process failure, before it happens
Keywords :
data analysis; eigenvalues and eigenfunctions; failure analysis; fault diagnosis; integrated circuit manufacture; integrated circuit technology; principal component analysis; process control; process monitoring; PCA eigenvectors; PCM parameters; data analysis; integrated circuit process line; principal component analysis; process control module; process control monitor; process failure diagnosis; process state space; Condition monitoring; Data analysis; Failure analysis; Independent component analysis; Integrated circuit testing; Phase change materials; Principal component analysis; Process control; Random processes; State-space methods; Principal component analysis (PCA); process control monitor (PCM); process failure analysis; process state space;
