A Scalable Framework for Defect Isolation of DNA Self-assemlbled Networks

Abstract

This paper presents and evaluates an approach for defect isolation of DNA self-assembled networks made of a large number of processing nodes. A previous framework based on a broadcast algorithm isolates defective nodes by using no redundancy (for the nodes) and an external defect map. Its disadvantage is the limited scalability, thus making it unsuitable for extremely large scale networks built through DNA self-assembly. Our framework improves upon the previous framework by involving three algorithmic!tiers; namely, I -hop wave expansion, eficient via placement, and unsafe node defection. The eficiency of the proposed framework is evaluated and compared with the original framework by considering large scale networks (up to 1000 x 1000 nodes), and a novel gross defect model (as well as the conventional random defect model assumed in previous manuscripts). Simulation results indicate that the proposed framework outperforms the original framework in broadcast latency and coverage, and shows excellent scalability features for DNA self-assembled nano-scale networks.

Fabrication Defects and Fault Models for DNA Self-Assembled Nanoelectronics

“…However, bottom-up DNA self-assembly tends to suffer from high defect rates [2]; therefore, breakthroughs are needed for defect modeling, defect diagnosis, and yield improvement at the nanoscale. Despite considerable research on fabrication methods [3][4][5][6], computing architectures [7], and error tolerance [8][9][10][11][12], little is known about the nature and types of defects in DNA selfassembly and their relative occurrences. Moreover, prior research on this topic has not addressed the impact of these defects on emerging nanoscale devices, logic gates, and circuit functionality.…”