Consortium Participants

  • Autodesk
  • GenoCAD
  • Gingko Bioworks
  • Globalfoundries
  • IBM
  • Intel
  • Mentor Graphics
  • Micron
  • Microsoft
  • Mubadala Technology
  • Raytheon BBN Technologies
  • SynBioBeta
  • Turner Designs Hydrocarbon Instr.
  • Twist Biosciences

  • Boise State University
  • Boston University
  • Brigham Young University
  • Columbia University
  • Dartmouth College
  • Georgia Institute of Technology
  • NCSU
  • UCLA
  • University of Illinois at Urbana-Champaign
  • UNC Greensboro
  • University of Washington

  • DoD
  • ONR
  • NIST
  • NSF

SemiSynBio Consortium and Roadmap Development

Semiconductor Synthetic Biology

Semiconductors enable the information technology infrastructure that we rely on for all aspects of our daily lives, including finance, transportation, energy, healthcare, education, communication, and entertainment systems and services. The remarkable trend described by Moore’s Law has driven increases in performance and function, while decreasing costs. Today, the semiconductor industry is facing fundamental physical limits and punishing increases in technology development and manufacturing costs. In order to realize the benefits of advances, including in the Internet of Things and Big Data, new approaches are required—for collecting, sharing, analyzing, and storing data and information. One such approach lies at the intersection of synthetic biology and semiconductor technology—the new field of Semiconductor Synthetic Biology, or SemiSynBio.

SemiSynBio aims to take advantage of the significant energy efficiency and information processing advantages that biological systems have over the most promising silicon-based systems. SemiSynBio may fundamentally redefine semiconductor design and manufacture, unleashing forces of creative disruption and giving rise to industries that bear little resemblance to those we know today. These advances build upon breakthroughs in DNA synthesis and characterization, electronic design automation, nanoscale manufacturing, and understanding of biological processes for energy efficient information processing.

SemiSynBio Consortium

A SemiSynBio Consortium is being established, comprising broad representation from relevant stakeholder communities. Consortium membership is open to all interested parties from the entire value chain and from both semiconductor and biotechnology industries. Participation by academic experts will ensure the project is informed by leading edge science and engineering in key disciplines. Government participation is invited from agencies whose missions align with, or can be realized through, research and development of advanced manufacturing in information systems—from sensors to information processing and storage.

The SemiSynBio Consortium is led by Semiconductor Research Corporation (SRC).

SemiSynBio Roadmap

With NIST AMTech support, the SemiSynBio Consortium is developing a SemiSynBio Roadmap, also under the leadership of SRC. This effort is underway, and is expected to be completed mid-2017. 

The timeframe for performing innovation research and transitioning results into production in the semiconductor and other industries is on the order of 10 years or longer. SemiSynBio technologies are likely beyond this time horizon. Therefore, the SemiSynBio Roadmap is expected to encompass up to 15 years. Such a long term view allows industry stakeholders to participate in accelerated progress while remaining individually competitive.

Technical Areas

The Roadmap is organized around five technical areas. Within these areas, it will establish technology targets and goals, assess the state of each technology area, identify challenges and barriers to advanced manufacturing, define quantitative objectives and metrics of progress, and prioritize research needs. The five technical areas are:

1. DNA-based Massive Information Storage

Device scaling and energy consumption during computation/storage has become a matter of strategic importance for modern Information and Communication Technologies (ICT). Molecular DNA memory has demonstrated storage density of ~1019 bit/cm3, several orders of magnitude higher than any other known storage technologies. One kg of DNA has a maximum theoretical storage capacity of ~2×1018 Mbit without error correction, which is equivalent to the total projected world’s storage requirement in 2035-2040 [i] [ii]. Increasing speed and decreasing cost of DNA synthesis and sequencing enable exploration of DNA storage technologies. The SSB Consortium will roadmap the production of large-scale, low-cost DNA memory systems that can be effectively integrated with semiconductor information technologies.

2. Energy Efficient, Cell-inspired and Cell-based Physical and Computational Systems

While electronic devices, such as transistors, can be made on the scale of nanometers, microprocessors and other integrated systems sizes are typically on the centimeter scale. Cellular “machines” that are sub-millimeter in size are capable of performing various functions using very little energy. The principles of cellular information processing need to be further understood in order to enable new generations of computing systems, possibly based on semiconductor or biological matter, or a combination. The roadmap will detail barriers to achieving extremely low-energy organic and inorganic computing and novel bio-inspired and biological circuits – so called 'cytomorphic systems' [iii].

3. Intelligent Sensor Systems

Live cells integrated with CMOS technology in a hybrid bio-semiconductor system have demonstrated high sensitivity and specificity at low operating energy. In addition, integration of biological or biologically inspired systems may offer alternative methods for providing energy to the chip. Self-powered, on-chip Intelligent Sensor Systems that integrate biological sensing functions and energy generation with inorganic information/computation capabilities enable diverse new applications. Example applications include fast, high throughput chemical screening for drug discovery, diagnosis and therapy planning for personalized medicine, and detecting chemical and biological agents for defense and environmental needs. Among the challenges to implementation of cell-semiconductor systems is maintaining cell viability on silicon.

4. Biological System Design

Leveraging advanced electronic design automation (EDA) tools and concepts for complex design can help to enable a radical increase in the complexity of biological design automation (BDA) capabilities. Alberto Sangiovanni-Vincentelli, a prominent academic researcher and co-founder of the two largest EDA companies (Cadence and Synopsys) sees a significant opportunity from extension of EDA “know how” into BDA [iv]. Roadmapping the fundamental barriers at the EDA/BDA interface requires expert input from the two communities.

5. DNA-controlled Sub-10 nm Manufacturing Challenge

Cells fabricate amazingly complicated new structures with high yield and low energy utilization. Biological assembly occurs at an assembly rate of ~1018 molecules per second (at biological growth rates a 1Gb chip could be built in about 5 s), and energy of ~10-17 J/molecule, which is 100X less than that in conventional subtractive manufacturing. Based on demonstrated DNA-controlled self-assembly of increasingly complex structures, such approaches have the potential for making complex sub-10 nm semiconductor structures. Also engineered microorganisms can potentially be used to produce a range of important chemicals and materials for semiconductor processes, such as novel low-K dielectric films. The challenge is extending these proofs of principle to more complex and relevant structures and systems.

  1. M. Hilbert and P. Lopez, "The world's technological capacity to store, communicate and compute information", Science 332 (2011) 60-65.
  2. Summary Report for the SRC Workshop on "Synergies between Semiconductors and Synthetic Biology" (SemiSynBio), Cambridge, MA, February 22-23, 2013,
  3. R. Sarpeshkar, Ultra Low Power Bioelectronics: Fundamentals, Biomedical Applications, and Bio-Inspired Systems (Cambridge University Press 2010)
  4. A. Sangiovanni-Vincentelli, "EDA meets Biology! The Bumpy Road Ahead", IEEE Design & Test of Computers 29 (2012) 49-50