Search State Extensibility based Learning Framework for Model Checking and Test Generation

The increasing design complexity and shrinking feature size of hardware designs have created resource intensive design verification and manufacturing test phases in the product life-cycle of a digital system. On the contrary, time-to-market constraints require faster verification and test phases; otherwise it may result in a buggy design or a defective product. This trend in the semiconductor industry has considerably increased the complexity and importance of Design Verification, Manufacturing Test and Silicon Diagnosis phases of a digital system production life-cycle. In this dissertation, we present a generalized learning framework, which can be customized to the common solving technique for problems in these three phases. During Design Verification, the conformance of the final design to its specifications is verified. Simulation-based and Formal verification are the two widely known techniques for design verification. Although the former technique can increase confidence in the design, only the latter can ensure the correctness of a design with respect to a given specification. Originally, Design Verification techniques were based on Binary Decision Diagram (BDD) but now such techniques are based on branch-and-bound procedures to avoid space explosion. However, branch-and-bound procedures may explode in time; thus efficient heuristics and intelligent learning techniques are essential. In this dissertation, we propose a novel extensibility relation between search states and a learning framework that aids in identifying non-trivial redundant search states during the branch-and-bound search procedure. Further, we also propose a probability based heuristic to guide our learning technique. First, we utilize this framework in a branch-and-bound based preimage computation engine. Next, we show that it can be used to perform an upper-approximation based state space traversal, which is essential to handle industrial-scale hardware designs. Finally, we propose a simple but elegant image extraction technique that utilizes our learning framework to compute over-approximate image space. This image computation is later leveraged to create an abstraction-refinement based model checking framework. During Manufacturing Test, test patterns are applied to the fabricated system, in a test environment, to check for the existence of fabrication defects. Such patterns are usually generated by Automatic Test Pattern Generation (ATPG) techniques, which assume certain fault types to model arbitrary defects. The size of fault list and test set has a major impact on the economics of manufacturing test. Towards this end, we propose a fault collapsing approach to compact the size of target fault list for ATPG techniques. Further, from the very beginning, ATPG techniques were based on branch-and-bound procedures that model the problem in a Boolean domain. However, ATPG is a problem in the multi-valued domain; thus we propose a multi-valued ATPG framework to utilize this underlying nature. We also employ our learning technique for branch-and-bound procedures in this multi-valued framework. To improve the yield for high-volume manufacturing, silicon diagnosis identifies a set of candidate defect locations in a faulty chip. Subsequently physical failure analysis an extremely time consuming step utilizes these candidates as an aid to locate the defects. To reduce the number of candidates returned to the physical failure analysis step, efficient diagnostic patterns are essential. Towards this objective, we propose an incremental framework that utilizes our learning technique for a branch-and-bound procedure. Further, it learns from