Enhanced Built-In Self-Diagnosis and Self-Repair Techniques for Daisy-Chain Design in MEDA Digital Microfluidic Biochips

Abstract

Digital microfluidic biochips have emerged as a promising alternative for various laboratory procedures in biochemistry such as drug discovery and DNA sequencing. A recent generation of digital biochips uses a micro-electrode-dotarray (MEDA) architecture, which provides finer controllability of droplets and seamlessly integrates microelectronics and microfluidics. To simplify the wiring design of such biochips, all microelectrodes and their control registers are daisy-chained together. Therefore, the ability to both identify faults in the chain and tolerate them is required in MEDA biochips. In this study, a new daisy-chain design approach is proposed, integrating a built-in self-repair scheme that can automatically detect faults and correct them. Moreover, an efficient test generation method that requires only a small number of test vectors is proposed to achieve 100% fault coverage without degrading the electrodes. The proposed self-repair scheme can be used in both offline and online modes. Experimental results show that detection and repair can be carried out for various types of faults that can occur in daisy chains.