Deep Q-Learning with Bit-Swapping-Based Linear Feedback Shift Register fostered Built-In Self-Test and Built-In Self-Repair for SRAM

Abstract

Including redundancy is popular and widely used in a fault-tolerant method for memories. Effective fault-tolerant methods are a demand of today’s large-size memories. Recently, system-on-chips (SOCs) have been developed in nanotechnology, with most of the chip area occupied by memories. Generally, memories in SOCs contain various sizes with poor accessibility. Thus, it is not easy to repair these memories with the conventional external equipment test method. For this reason, memory designers commonly use the redundancy method for replacing rows–columns with spare ones mainly to improve the yield of the memories. In this manuscript, the Deep Q-learning (DQL) with Bit-Swapping-based linear feedback shift register (BSLFSR) for Fault Detection (DQL-BSLFSR-FD) is proposed for Static Random Access Memory (SRAM). The proposed Deep Q-learning-based memory built-in self-test (MBIST) is used to check the memory array unit for faults. The faults are inserted into the memory using the Deep Q-learning fault injection process. The test patterns and faults injection are controlled during testing using different test cases. Subsequently, fault memory is repaired after inserting faults in the memory cell using the Bit-Swapping-based linear feedback shift register (BSLFSR) based Built-in Self-Repair (BISR) model. The BSLFSR model performs redundancy analysis that detects faulty cells, utilizing spare rows and columns instead of defective cells. The design and implementation of the proposed BIST and Built-in Self-Repair methods are developed on FPGA, and Verilog’s simulation is conducted. Therefore, the proposed DQL-BSLFSR-FD model simulation has attained 23.5%, 29.5% lower maximum operating frequency (minimum clock period), and 34.9%, 26.7% lower total power consumption than the existing approaches.

Memory built-in self-repair and correction for improving yield: a review

“…The efficiency factor indicates the main goal achieved by the algorithm or implemented the feature in it. Area overhead efficient [11], [23], [29], [31]- [33], [37], [46], [47], [72], [73] 2…”