An intelligent function level backward state justification search for ATPG.

Abstract

This dissertation describes an innovative approach to the state justification portion of the sequential circuit automatic test pattern generation (ATPG) process. Given the absence of a stored fault an ATPG controller invokes some combinational circuit test generation procedure, such as the D-algorithm, to identify a circuit state (goal state) and input vectors that will sensitize a selected fault. The state justification phase then finds a transfer sequence to the goal from the present state. A forward fault propogation search can be successfully guided through state space from the present state but the forward justification search is less efficient and the failure rate is high. The backward function level search invokes inverse RTL level primitives and exploits easy movement of data vectors in structured VLSI circuits. Examples illustrated are in AHPL. This search is equally applicable to an RTL level subset of VHDL. Combinational logic units are treated as functions and the circuit states are partitioned into control states and data states. The search proceeds backwards over the control state space starting from the goal state node and data states are transformed according to the control flow. Vectorized data paths in VLSI circuits and search guiding heuristics which favor convenient inverse functions keep the number of search nodes low. Partial covers, conceptually similar to singular covers in D-algorithm, model the inverse functions of combinational logic units. The search successfully terminates when a child state node logically matches the present state and the present state values can satisfy all the constraints encountered along the search path.