The first step when a customer return occurs in industry is to reproduce the failure mechanism with any original test and test conditions (voltage, temperature, etc.) used during initial post-fabrication test. Depending on the test scenario (test sequence, test scheme, test conditions), different types of defects and failure mechanisms are targeted, thus leading to different diagnosis outcomes. Based on our previous work, this paper presents a comprehensible framework for cell-aware diagnosis of customer returns. The first part of the paper summarizes our previous work in which a generic cell-aware diagnosis flow using a Bayesian classification method was proposed to precisely identify defect candidates in combinational cells of a defective circuit according to a given test scenario. The second part of the paper extends the previous work by dealing with defects in sequential cells of defective circuits. New experiments done on a silicon test chip and a customer return with a given test scenario have proven the efficacy of our flow in terms of diagnosis accuracy and resolution.