2018 Semiconductor Synthetic Biology Roadmap

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2018 SemiconductorSynthetic BiologyRoadmap

Editor’s NoteVictor ZhirnovChief Scientist at SemiconductorI am delighted to introduce the 1st Edition of the SemiSynBio Roadmap, a collectiveResearch Corporation and Editor ofwork by many dedicated contributors from industry, academia and government.the 1st Edition of the SemiconductorIt can be argued that innovation explosions often occur at the intersection ofRoadmap.scientific disciplines, and Semiconductor Synthetic Biology or SemiSynBio is anexcellent example of this. The objective of this Roadmap is to serve as a vehicleto realize the transformative potential of the new technology emerging at theinterface between semiconductors and synthetic biology. The SemiSynBio Roadmapis intended to catalyze both interest in and rapid technological advances thatprovide new capabilities that benefit humankind.Victor Zhirnov is Chief Scientist at the Semiconductor Research Corporation. His researchinterests include nanoelectronics devices and systems, properties of materials at thenanoscale, bio-inspired electronic systems etc. He has authored and co-authored over100 technical papers and contributions to books. Victor Zhirnov served as the Chair of theEmerging Research Device (ERD) Working Group for the International Technology Roadmapfor Semiconductors (ITRS). Victor Zhirnov also holds adjunct faculty position at North CarolinaState University and has served as an advisor to a number of government, industrial, andacademic institutions. Victor Zhirnov received the M.S. in applied physics from the UralPolytechnic Institute, Ekaterinburg, Russia, and the Ph.D. in solid state electronics andmicroelectronics from the Institute of Physics and Technology, Moscow, in 1989 and 1992,respectively. From 1992 to 1998 he was a senior scientist at the Institute of Crystallography ofRussian Academy of Science in Moscow. From 1998 to 2004 he was research faculty at NorthCarolina State University. He joined SRC in 2004.i

Table of ContentsAcronym Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2Chapter 1DNA-based Massive Information Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Chapter 2Energy Efficient, Small Scale Cell-Based and Cell-inspired Information Systems . . . . 7Chapter 3Intelligent Sensor Systems and Cell/Semiconductor Interfaces . . . . . . . . . . . . . 12Chapter 4Electronic-Biological System Design Automation . . . . . . . . . . . . . . . . . . . . . . 17Chapter 5Biological Pathways for Semiconductor Fabrication and Integration . . . . . . . . . . 23Chapter 6Ongoing Impact of the SemiSynBio Roadmap . . . . . . . . . . . . . . . . . . . . . . . . 29Industrial Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32ii

Acronym Definitions2DTwo-DimensionalHSMHybrid-State Machine3DThree-DimensionalMFCMicrobial Fuel CellAIArtificial IntelligenceMISTMolecular Information StorageAROArmy Research OfficeNAMNucleic Acid MemoryBEMBioelectronic MedicineNISTNational Institure of Standards and TechnologyBDABio-Design AutomationNSFNational Science FoundationCADComputer-Aided DesignONROffice of Naval ResearchCMOSComplementary Metal-Oxide-SemiconductorR&DResearch And DevelopmentEDAElectronic Design AutomatinREXCOMRoadmap Executive CommitteeDMSODimethyl SulfoxideRFRadio-FrequencyDNADeoxyribonucleic AcidRNARibonucleic AcidDoDDepartment of DefenceSEMISYNBIO Semiconductor Syntetic BiologyEBExabyteSDASoftware Design AutomationGbGigabitSNRSignal-to-Noise RatioGBGigabyteSRAMStatic Random-Access MemoryGPGenome ProjectSRCSemiconductor Research CorporationIARPAIntelligence Advanced Research Projects ActivityTBTerabyteIDCInternational Data CorporationTHFTetrahydrofuranISSIntelligent Sensor SystemTWGTechnical Working GroupITInformation TechnologyZBZettabyteHDDHard Disk Drive1

IntroductionSemiconductors enable the information technology infrastructure that we rely on for all aspects of our daily lives,including financial, transportation, energy, healthcare, education, communication and entertainment systems andservices. The remarkable trend described by Moore’s Law has driven increases in performance and function, whiledecreasing costs. Today, the semiconductor industry is facing fundamental physical limits and punishing increasesin technology development and manufacturing costs. In order to realize the benefits of the internet of thingsand “big data”, new approaches to collecting, sharing, analyzing and storing data and information are required.One such approach lies at the intersection of synthetic biology and semiconductor technology — the new field ofSemiconductor Synthetic Biology, or SemiSynBio.SemiSynBio aims to take advantage of the significant energyas a planning tool that connects the societal trends andefficiency and information processing advantages thatchallenges facing a product or industry with the technologiesbiological systems have over the best foreseeable equivalentneeded to address them. It is also intended to help guide thesilicon-based systems. SemiSynBio may fundamentallyfuture investments in this emerging field.redefine semiconductor design and manufacture, unleashingforces of creative destruction and giving rise to industriesthat bear little resemblance to that which we know today. ToThe SemiSynBio Roadmap identifies technology targets/goalsin the following five technical areas:fuel such an industry, not only is technical innovation required1. DNA-based Massive Information Storage.but equally important development of the future workforce.2. Energy Efficient, Small Scale Cell-Based and Cell-inspiredThese advances build upon breakthroughs in DNA synthesisand characterization, electronic design automation, nanoscalemanufacturing, and understanding of biological processes forenergy efficient information processing.In order to realize the transformative potential of thenew technology emerging at the interface betweensemiconductors and synthetic biology, an industry-Information Systems.3. Intelligent Sensor Systems and Cell/SemiconductorInterfaces.4. Electronic-Biological System Design Automation.5. Biological pathways for semiconductor fabrication andintegration.led consortium — SemiSynBio was formed in 2015. TheTo develop a comprehensive Technology Roadmap forSemiSynBio consortium includes stakeholders from all partsSemiSynBio, joint efforts of experts from different disciplinesof the value chain and include semiconductor manufacturers,have been employed: biology, chemistry, computer science,biotech and pharmaceutical companies, IT industry, softwareelectrical engineering, materials science, medicine, physics,providers, and EDA and BDA companies. The consortium alsoand semiconductor technology.includes universities and government agencies. The long-termgoals of SemiSynBio Consortium are to advance the emergingThe SemiSynBio Technology Roadmap addresses a 20-yearSemiSynBio technology through industry-led precompetitivetimeframe, embracing both current and projected needs. Itresearch and development. A critical activity for theserves as a guide for university researchers who will trainconsortium has been the development of a SemiSynBiothe entrepreneurs, engineers and scientists who will leadTechnology Roadmap. The Roadmap contains an overview ofthe creation of this new industry. It is expected that manythe status of the research in SemiSynBio, describes salientstartups emerge from the research to commercialize theseoutcomes to date, and outlines research challenges thatnew approaches.can now be foreseen. This Roadmap is intended to serve2

Chapter 1: DNA-based Massive Information StorageChapter IDNA-based MassiveInformation Storage1. IntroductionFigure 1.1: Estimated and projected globalmemory demand — including a conservativeestimate and an upper bound [2]–[7]Information has been the social-economic growth engine1.E 30of civilization since the very beginning, and informationproduction correlates with social well-being and economic1.E 29growth. Currently, the production and use of information has1.E 28been grown exponentially, and by 2040 the estimates for thefrom the International Data Corporation (IDC) reports [2]–[6]and the work by Xu [7].Today, the world is creating data at a much faster rate thanstorage technologies can handle. There is a risk that within10-15 years, buying exponentially more storage capacity willou1.E 241.E 231.E 221.E 21due to limited materials supply, e.g. silicon wafers). Currently,1.E 202000i) Optical, ii) Magnetic (HDD & Tape), and iii) Solid-State (e.g.pe1.E 25become prohibitively expensive (and potentially impossiblethere are three main paradigms for data storage (Figure 1.2):rb1.E 26Upresearch by Hilbert and Lopez [1]). The data points are takenTotal stored bitsbits as shown in Figure 1.1 (these estimates are based onnd1.E 27worldwide amount of stored data are between 1024 and1028[5][4][3][2]2010tema[7] [6]etivtiesvaersnCo20202030Year204020502060Flash). Their feature sizes are already close to the physical limitsof scaling and further improvement in storage density can beIn the search for potential solutions to this problem, multipleachieved only through 3D integration. However, even in thestudies have used DNA and other synthetic polymers, tocase of an extreme 3D packing the potential for improvementexplore the use of sequence-controlled polymers as theis limited. Therefore, the world is facing a serious data storagebasis for molecular information storage technologies (MIST).problem that cannot be resolved by current technologies.3

SemiSynBio RoadmapMolecular media offers far greater potential for scalingexponentially, e.g. 107 above the best expectations for flash2. Key challengesor magnetic storage (Figure 1.2). DNA can store informationA number of recent studies have shown that DNA can supportstably at room temperature for hundreds of years with zeroscalable, random-access and error-free information storagepower requirements, making it an excellent candidate for[9]–[15]. Current DNA storage workflows take weeks tolarge-scale archival storage [8].write and then read data due to reliance on life sciencesFigure 1.2: The volumetric information densityof conventional storage media vs. DNA1.00E 10technologies that were not designed for use in the samesystem. The current workflows are too slow and costly tosupport exascale archival data storage. Solving this problemwill require: (i) Substantial reductions in the cost of DNA1.00E 09synthesis and sequencing, and (ii) Deployment of these1.00E 08technologies in a fully automated end-to-end workflow.1.00E 07Gbit/mm31.00E 06In summary, the two major categories of technical challengesPotential of DNA:107 improvement1.00E 05include:a. P hysical Media: Improving scale, speed, and cost of1.00E 04synthesis and sequencing technologies.1.00E 03b. Operating System: Creating scalable indexing, random1.00E 02access and search capabilities.1.00E 011.00E shDNALimitDuring 2016 and 2017, Intelligence Advanced ResearchProjects Activity (IARPA) and the Semiconductor ResearchCorporation (SRC) organized two workshops that assembledinternational stakeholders from academia and the biotech,semiconductor and information technology industries toroadmap clear and achievable engineering optimizationsthat would be necessary to develop scalable MIST systems.In 2018, IARPA announced a MIST program that seeks to putthis roadmap into practice by assembling a multidisciplinarycommunity around the shared goal of developing compactand scalable molecular information storage technologies tosupport real-world “big data” use cases [1]. This roadmap isconsistent with the goals of the MIST program. It is expectedthat both small & medium-sized enterprises as well as largecompanies will participate in MIST developments.Information has been thesocial-economic growth engineof civilization since the verybeginning, and informationproduction correlates with socialwell-being and economic growth.3. Key Technical Areas3.1. StorageThe 2019-2022 goal of this technical area is to demonstratea fully automated storage system capable of writinginformation to the polymer media with a high throughput,low cost, and writing accuracy that enable subsequentrandom access and error-free decoding of files. Possiblestorage media include, but are not limited to, DNA, peptides,or synthetic polymers. The projected storage capacity trendfor MIST is shown in Figure 1.3.4

Chapter 1: DNA-based Massive Information StorageFigure 1.4: MIST DNA synthesis cost targetsFigure 1.3: MIST storage capacity growth projection1.E 251.E 221.E 190.11 ZB0.0011 EB /bitsBits0.000011.E 161.E 131.E 100.000000011 TB1 GB1E-091.E 072019 2021 2023 2025 2027 2029 2031 2033 2035Year1E-112019202020212022Year20232024Methods for writing data to polymers include, but are notSpectrometry. Important considerations for developmentlimited to, de novo chemical synthesis, de novo enzymaticare resource requirements to read each decodable bit ofsynthesis, or selective editing of existing sequences (for ainformation, bit depth, maximum read error rate, maximumdetailed discussion see the SemiSynBio Roadmap reportsread throughput for decodable data, time to first byte[16] and [17]). Important considerations for developmentafter a read request, and compatibility with available writeare resource requirements to write each decodable bit ofapproaches. The MIST write and read speed projections areinformation, maximum write error rate, maximum writeshown in Figure 1.5.throughput for decodable data, total storage capacity,longevity of stored data, and compatibility with availableFigure 1.5: MIST Write and Read speed projection1.E 10retrieval approaches.A dramatic reduction in cost of DNA synthesis or syntheticpolymers is mandatory for practical MIST systems. Technical1.E 08approaches for the cost reduction are discussed in [17].ReadFigure 1.4 displays DNA synthesis cost targets as formulatedin the MIST program [18].The 2019-2022 goal of this technical area is to demonstrate aWritebit/s3.2. Retrieval1.E 061.E 04fully automated device capable of reading information storedin the polymer media with high throughput, low cost, andread accuracy sufficient to enable random access and error-1.E 02free decoding.Methods for reading data from polymers include, but arenot limited to, Sequencing By Synthesis, Single MoleculeRealTime Sequencing, Nanopore Sequencing or Mass51.E 002019 2020 2020 2021 2021 2022 2022 2023 2023 2024Year

SemiSynBio Roadmap3.3. Operating SystemThe 2019-2022 goal of this technical area is todemonstrate an operating system that coordinatesTable 1.1: Targets for MIST operating system development2019scalable and efficient bulk write/read and randomaccess workflows. The targets for the operating systemdevelopment are shown in Table 1.1.2020 Development of a simulator of molecular storage and retrievaldevices Demonstrated robustness to anticipated failure modes of storageand retrieval devices; Demonstrated indexing, addressing, decoding and random accesscapabilities that plausibly scale into the exabyte regime.Important considerations for operating systemdevelopment include: i) resource requirements forfile addressing and encoding with molecular media, ii)2021performance of algorithms for physically organizing Operating system capabilities are further improved, refined, andoptimized for practical applications; Tools development for extreme compression and approximatereconstruction of multimedia data.media by file type or other properties, iii) specificresource requirements for error correction and randomaccess of files, and iv) overall resource requirements forreconstruction of files.2022 Support for content-addressable memory, or pattern-based searchover the content of a molecular archive; Support for security access control, such as the ability to dynamicallyset unique policies per asset and/or per user.References[1] M. Hilbert and P. Lopez, “The World’s Technological Capacity to Store, Communicate, and Compute Information,” Science 332 (2011) 60.[2] J. Gantz and D. Reinsel, “The Digital Universe Decade – Are You Ready?” - International Data Corporation Report 2010: digital-universe-are-you-ready.pdf[3] J. Gantz and D. Reinsel, “Extracting Value from Chaos State of the Universe” - International Data Corporation Report 2011: xtracting-value-from-chaos-ar.pdf.[4] J. Gantz and D. Reinsel, “THE DIGITAL UNIVERSE IN 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East” - International Data CorporationReport 2012: -digital-universe-united-states.pdf[5] V. Turner and J. Gantz, “The Digital Universe of Opportunities” - EMC/IDC Report 2014. -digital-universe-2014.pdf[6] D. Reinsel, J. Gantz, and J. Rydning, “Data Age 2025: The Evolution of Data to Life-Critical IDC Report 2017. ds/files/Seagate-WP-DataAge2025-March-2017.pdf[7] Z.-W. Xu, “Cloud-Sea Computing Systems: Towards Thousand-Fold Improvement in Performance per Watt for the Coming Zettabyte Era,” J. Comput. Sci. Technol. 29(2014) 177.[8] V. Zhirnov, R. M. Zadegan, G. S. Sandhu, G. M. Church, and W. L. Hughes, “Nucleic acid memory,” Nature Mater. 15 (2016) 366.[9] G. M. Church, Y. Gao, and S. Kosuri, “Next-Generation Digital Information Storage in DNA,” Science 337 (2012) 1628.[10] N. Goldman, P. Bertone, S. Y. Chen, C. Dessimoz, E. M. LeProust, B. Sipos, E. Birney, “Towards practical, high-capacity, low-maintenance information storage insynthesized DNA,” Nature 494 (2013) 77.[11] S. L. Shipman, J. Nivala, J. D. Macklis, and G. M. Church, “CRISPR–Cas encoding of a digital movie into the genomes of a population of living bacteria,” Nature 547(2017) 345.[12] J. Bornholt, R. Lopez, D. M. Carmean, L. Ceze, G. Seelig, and K. Strauss, “Toward a DNA-Based Archival Storage System,” IEEE Micro 37 (2017) 98.[13] J. Bornholt, R. Lopez, D. M. Carmean, L. Ceze, G. Seelig, and K. Strauss, “A DNA-Based Archival Storage System,” in Proceedings of the Twenty-First InternationalConference on Architectural Support for Programming Languages and Operating Systems - ASPLOS ’16, 2016, pp. 637–649.[14] Y. Erlich and D. Zielinski, “DNA Fountain enables a robust and efficient storage architecture”, Science 355 (2017) 950[15] S. M. H. T. Yazdi, R. Gabrys, and O. Milenkovic, “Portable and Error-Free DNA-Based Data Storage.,” Sci. Rep. 7 (2017) 5011.[16] D. Markowitz, “SRC/IARPA Workshop on DNA-based Massive Information Storage,” 2016. [Online]. Available: storage-final-twg1.pdf.[17] B. Bishop, N. Mccorkle, and V. Zhirnov, “Technology Working Group Meeting on future DNA synthesis technologies,” 2017. [Online]. Available: storage-final-twg1-4-18.pdf. [Accessed: 03-Aug-2018].[18] “IARPA BAA on Molecular Information Storage.” [Online]. Available: mist/mist-baa. [Accessed: 03-Aug-2018].6

Chapter 2: Energy Efficient, Small Scale Cell-Based and Cell-inspired Information SystemsChapter 2Energy Efficient, Small ScaleCell-Based and Cell-inspiredInformation Syst

5. Biological pathways for semiconductor fabrication and integration. To develop a comprehensive Technology Roadmap for SemiSynBio, joint efforts of experts from different disciplines have been employed: biology, chemistry, computer science, electrical engineering, materials science, medicine, physics, and semiconductor technology.

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