On July 16, 2018, the National Science Foundation (NSF) announced a $12 million investment in the Semiconductor Synthetic Biology for Information Processing and Storage Technologies (SemiSynBio) program, a partnership between NSF and Semiconductor Research Corporation (SRC).  Researches expect that integrating biological structures with semiconductor technology could increase current data storage capabilities by 1,000 times, while using less energy than current technology.  "While today's data storage devices are smaller and more powerful than ever before, we have the potential to catalyze a new wave of innovation that will push the boundaries for the future," stated Erwin Gianchandani, acting NSF assistant director for Computer and Information Science and Engineering (CISE).  Further, "[t]his research will pave the way for devices with much greater storage capacity and much lower power usage. Imagine, for example, having the entire contents of the Library of Congress on a device the size of your fingernail."   The funded projects include:

• DNA-based electrically readable memories:  Joshua Hihath, University of California-Davis; Manjeri Anantram, University of Washington; Yonggang Ke, Emory University.
• An on-chip nanoscale storage system using chimeric DNA:  Olgica Milenkovic, University of Illinois at Urbana-Champaign.
• Highly scalable random access DNA data storage with nanopore-based reading:  Hanlee Ji, Stanford University. 
• Nucleic Acid Memory: William Hughes, Boise State University.
• Very large-scale genetic circuit design automation:  Christopher Voigt, Massachusetts Institute of Technology; Kate Adamala, University of Minnesota-Twin Cities; Eduardo Sontag, Northeastern University.
• Redox-enabled Bio-Electronics for Molecular Communication and Memory (RE-BIONICS):  William Bentley, University of Maryland College Park.
• YeastOns:  Neural Networks Implemented in Communicating Yeast Cells: Rebecca Schulman, Johns Hopkins University; Eric Klavins, University of Washington; Andrew Ellington, University of Texas at Austin.
• Cardiac Muscle-Cell-Based Coupled Oscillator Networks for Collective Computing:  Pinar Zorlutuna, University of Notre Dame.