With the ever increasing volume of digital information, it is estimated that the global memory demand will exceed the projected silicon supply by 2040. Critical factors for the information and communication industries are the scaling and energetics of information storage materials. One mechanism to expedite materials discovery for memory applications is to consider unconventional sources that have unique characteristics unavailable in other material systems. Nucleic acids, for example DNA, chemically encode information with a base-4 quaternary code (A,T,C,G). Nucleic Acid Memory (NAM) is a potential alternative to silicon based memory because its volumetric density is 1000 times greater and its energy of operation is 100M times less than flash memory – the industry standard. Rapid progress in DNA synthesis and sequencing has enabled reading and writing of arbitrary digital formats.Although ~100% recovery of encoded information has been demonstrated, the existing algorithms are not universal nor do they compensate for naturally occurring defects. Defects, including the loss of individual bases (letters) and complete sequences (words), are expected during NAM writing (synthesis), reading (sequencing), and storage. Our objective is to develop efficient and stable error correction algorithms for DNA that: (1) compensate for naturally occurring defects, and (2) account for synthesis constraints such as GC content and sequence repetition. This study is in pursuit of non-biological and nonvolatile NAM applications.
- SRC STARnet- FAME 2015
- Micron (2015)