Genetic Algorithm-Based Digital Test Optimization Method and its Application to Yield Improvement

Narrowing design and manufacturing process margins with technology scaling are one of the causes for a reduction in IC chip test margin. This situation is further aggravated by the extensive use of third-party design blocks in contemporary system-on-chips which complicates chip timing constraint. Since a thorough timing verification prior to silicon fabrication is usually not done due to aggressive product launch schedules and escalating design cost, occasionally, a post-silicon timing optimization process is required to eliminate false fails encountered on ATE. An iterative two-dimensional shmoo plots and pin margin analysis are custom optimization methods to accomplish this. However, these methods neglect the interdependencies between different IO timing edges such that a truly optimized condition cannot be attained. In this paper, we present a robust and automated solution based on a genetic algorithm approach. Elimination of shmoo holes and widening of test margins (up to 2x enhancements) are demonstrated on actual product test cases. Besides test margin optimization, this method also offers insights into the criticality of test pins to accelerate failure debug turnaround time.

Test Data Generation and Selection Using Levy Flight-Based Firefly Algorithm

This article discusses the application of an improved version of the firefly algorithm for the test suite optimization problem. Software test optimization refers to optimizing test data generation and selection for structural testing criteria for white box testing. This will subsequently reduce the two most costly activities performed during testing: time and cost. Recently, various search-based approaches proved very interesting results for the software test optimization problem. Also, due to no free lunch theorem, scientists are continuously searching for more efficient and convergent methods for the optimization problem. In this paper, firefly algorithm is modified in a way that local search ability is improved. Levy flights are incorporated into the firefly algorithm. This modified algorithm is applied to the software test optimization problem. This is the first application of Levy-based firefly algorithm for software test optimization. Results are shown and compared with some existing metaheuristic approaches.

Test optimization selection method based on NSGA-3 and improved Bayesian network model

Most of the solutions to existing test selection problems are based on single-objective optimization algorithms and multi-signal models, which maybe lead to some problems such as rough index calculation and large solution set limitations. To solve these problems, a test optimization selection method based on NSGA-3 algorithm and Bayesian network model is proposed. Firstly, the paper describes the improved Bayesian network model, expounds the method of model establishment, and introduces the model's learning ability and processing ability on uncertain information. According to the constraints and objective functions established by the design requirements, NSGA-3 is used to calculate the test optimization selection scheme based on the improved Bayesian network model. Taking a certain component of the missile airborne radar as an example, the fault detection rate and isolation rate are selected as constraints, and the false alarm rate, misdiagnosis rate, test cost, and test quantity are the optimization goals. The method of this paper is used for test optimization selection. It has been verified that this method can effectively solve the problem of multi-objective test selection, and has guiding significance for testability design.

METHOD FOR SPLITTING THE FEASIBLE REGION TO INCREASE THE VARIABILITY OF CONTINUOUS OPTIMIZATION TEST PROBLEMS

The solution of optimization problems is essential for the design and control of technical systems. The optimization problem arising in practice has a high dimension, nonlinear criteria, and constraints. There are a lot of continuous optimization tasks for testing and research of optimization algorithms performance. These tasks have a convex range of acceptable values limited to a specified range for each parameter. The problem of generating test multidimensional continuous optimization tasks with nonlinear constraints and splitting the feasible region is considered. A method was proposed for splitting the feasible region by separate domains using the multidimensional grid of forbidden solutions. As a result, the problem acquires properties closer to optimizing technical systems with complex constraints. The method allows creating an unlimited number of test optimization problems, which can be used to research and develop optimization algorithms. The method is simple to implement, and the impact on the computational complexity of tasks is insignificant. Research has been carried out on four widely used continuous single-objective optimization test functions, with the Genetic algorithm and the Particle Swarm Optimization algorithm. It is shown that the proposed method has an influence on the process of solving multidimensional continuous optimization problems by population algorithms and on the dependence of the accuracy of the algorithm on its heuristic coefficients.