An Advanced BIRA for Memories With an Optimal Repair Rate and Fast Analysis Speed by Using a Branch Analyzer

As memory capacity and density grow, a corresponding increase in the number of defects decreases the yield and quality of embedded memories for systems-on-chip as well as commodity memories. For embedded memories, built-in redundancy analysis (BIRA) is widely used to solve quality and yield issues by replacing faulty cells with healthy redundant cells. Many BIRA approaches require extra hardware overhead in order to achieve optimal repair rates, or they suffer a loss of repair rate in minimizing the hardware overhead. An innovative BIRA approach is proposed to achieve optimal repair rates, lower area overhead, and increase analysis speed. The proposed BIRA minimizes area overhead by eliminating some storage coverage for only must-repair faulty information. The proposed BIRA analyzes redundancies quickly and efficiently by evaluating all nodes of a branch in parallel with a new analyzer which is simple and easy-to-implement. Experimental results show that the proposed BIRA allows for a much faster analysis speed than that of the state-of-the-art BIRA, as well as the optimal repair rate, and relatively small area overhead.