Towards a Distributed Network for Nano-fabrication and -characterization: Lessons Learned from the NNIN

Transcription

1 Towards a Distributed Network for Nano-fabrication and -characterization: Lessons Learned from the NNIN Roger T. Howe, NNIN Director Building an Integrated MGI Accelerator Network Georgia Tech June 5-6, 2014

2 NNIN s Mission logo l The National Nanotechnology Infrastructure Network (NNIN) was established in 2004 to provide rapid, costeffective access to expensive nanotechnology toolsets for all US academic and industrial researchers l Key features: u Access usually involves training researchers to become proficient hands-on users, in addition to providing expert advice on using the Network s shared tool-set to achieve their goals u NNIN Facilities are User Facilities not Research Centers l NNIN s focus: the external user someone not affiliated with the host university 2

3 The Scope of the Network 14 university- based nanotechnology facili8es working together to provide geographically distributed, facilitated access for nanotechnology researchers from academic, government, and companies across the US 3

4 NSF Investments in Nano Infrastructure, National Research and Resource Facility for Submicron Structures (NRRFSS), Cornell University National Nanofabrication Facility, Cornell University National Nanofabrication Users Network (NNUN): Cornell, Stanford, Howard, Penn State, and UC Santa Barbara National Nanofabrication Infrastructure Network (NNIN): Cornell, Stanford, Howard, Penn State, UCSB, Harvard, Georgia Tech, U.Wash., U.Minn., U.Mich., UT Austin, UNM UNM dropped, Wash. U., CU-Boulder, ASU added NG-NNIN competition ; no-win outcome NSF regroups: public comment period ended on June 2,

5 What Capabilities are in the Network? Clean rooms and other labs with a broad range of tools to support nanotechnology research Nanolithography: 11 sites have advanced electron-beam lithography Deposition: sputtering, evaporation, sputtering, ALD, Etching: ion mills, plasma etchers, deep reactive ion etchers, Polymer-based fabrication: PDMS molding, Specialized capabilities at selected sites, such as Dip-pen patterning (Michigan) Nanoparticle synthesis (Washington Univ., St. Louis) Sol-gel ferroelectric oxide deposition (Penn State) Nanoimprint lithography (University of Texas) Computational modeling (Harvard, Cornell, Michigan, and Stanford) Supported nanotechnologies Nanoscale electronics, photonics, and electromechanical systems Nanoscale materials research Bio-MEMS and nanoscale biosensors and much more! 5

6 NNIN Site Focus Areas Table 1---NNIN sites and technical competencies and leadership areas L=Leadership, x=assigned technical areas Bio & Integrated Systems Chem. & Molecular-Scale Tech. Electronics, Optics & MEMS Bio & Life Sciences Materials & Physical Sciences Computation Geo-Sciences Cornell x x L x L L x x L x x L L L x Man f Res. Support/Tool Dev Remote Usage Support Inorganic-Organic Interface Energy Precision Sciences & Engineering Environment & Health Society & Ethics Education Diversity Outreach Stanford x x L x L L x x L x x x x L L x Georgia Tech x x L x x x L L Michigan L x x L L x x x x Harvard x L x x L x x x x UCSB x L x x L L Minnesota x x x x L x x Penn State L x x x x x x Texas L x L x x Washington x L L x x x x Howard x x x L ASU x x x L x L WUStL x x L x L Colorado x x L L x L 6

7 NNIN Sites ( ) Washington Univ. U. Michigan Penn State U. Washington U. Minnesota Stanford Cornell. UCSB Harvard Arizona State U. Texas Austin Georgia Tech U. Colorado U. Colorado Howard U. Howard U. 7

8 NNIN Resources Supporting User Research l Over 1100 advanced tools to support nanotechnology research and development u Lithography u Deposition and growth u Etching u Characterization u Soft material processes u Back end processes u Particle technology l >175 FTE technical staff with >2400 person-years of experience u Maintenance u Process development u Process support u Project support u Training n n 2200 new users (persons) per year 10s of thousands of individual training sessions A uniquely effective environment for support of nanotechnology research and development 8

9 How Does a User Project Happen? Personal Contact Web/ phone Conferences Other Users Prompt Response from Site User Staff Consulta8on with NNIN Staff Max. 2 pg. Proposal/ Project Plan/ MOU One to three weeks elapsed 8me, depending on user s 8meline, Scheduling (~2 wk goal) Pre- visit Planning and Training as appropriate Hands on training: Safety training Society and Ethics tutorial Staff Consulta8on Process Integra8on Timely equipment training > 2200 new users trained in 2013 on a large equipment set Goals: Evolu8on to independent user Useful structures by end of first visit 9

10 NNIN Cooperative Agreement Budget 10

11 Nanotechnology Facility Operations Plus 10 New Buildings or Laboratories in 10 Years $500 Million Investment by Universities and States ~ $79M Annual Opera8ng Expenses 11

12 NNIN s User Community in

13 NNIN Annual Users: large companies 770 small companies 806 other universi8es 13

14 Trends in User Technical Fields Life sci. Chemistry Physics MEMS Materials Op8cs Electronics Electronics 14

15 NNIN Broad Impacts Ø Increases US research productivity by enabling faculty and students at all institutions access to advanced nanotechnology instrumentation Ø Increases US industrial competitiveness by enabling small companies to prototype their innovations Ø Builds the nanotechnology workforce with over 5000 academic users, most of whom are graduate students Ø Strengthens the nano facilities at member universities, including computational modeling Ø Provides a platform for an education and outreach program reaching K-Gray Ø Provides a platform for proactive consideration of societal impacts of nanotechnology 15

16 NNIN s Impact on Commercialization l Small company/nnin Scenarios u Spin-offs of technology developed (by faculty) at the NNIN site university u u n Benefit from NNIN both before and after spin-off Companies founded on technology invented at other universities but developed using NNIN facilities Corporate developed or licensed technology further developed using NNIN l Key factors to success u Advanced Technology u Accessible Technology u Affordable Technology u Reliable Technology/redundancy u Process flexibility u Minimum IP encumbrance u Geography somewhat important but small companies often locate a person at the NNIN site to work full time l Small company users have a different use profile than most other users u Broader spectrum of tools u More experienced/less need for instruction u More complex integration u Deadlines u Less tolerant of downtime/process issues l Measures of success u >350 companies used NNIN in 2013 u At least two IPOs n Pacific Biosciences n InvenSense 16

17 NNIN s Industrial User Community 111 industrial users of NNIN were contacted with a survey on use of the network s facili8es 86 responded with at least a par8ally completed survey Thema Monroe- White (Ga. Tech.) and Kurt E. Petersen (NNIN Advisory Board Member), NNIN Economic Impact Survey Report, April 15,

18 NNIN and Nanotechnology Commercialization Prototyping is the major benefit of access to an NNIN facility Access to NNIN facili8es is essen3al to these companies Number of industrial users in 2013: about 750 Thema Monroe- White (Ga. Tech.) and Kurt E. Petersen (NNIN Advisory Board Member), NNIN Economic Impact Survey Report, April 15,

19 Small Company Impact 770 users in year 10 from small companies > 350 small companies/year Prototype silicon gyroscope fabricated in SNF around 2004 Profitable by around 2007 SBIR/STTR Venture University Spin- outs Ini8al public offering, Nov. 16, 2011 $75M raised at $7.50/share June 4, 2014 price: $18.83/share Market Capitaliza8on: $1.66B 19

20 NNIN s Computation Program (NNIN/C) 1 nm 10 nm 100 nm 1000 nm (1 μm) ab ini5o electronic structure 10 Photonics Molecular Dynamics Microfluidics and MEMS Dr. Michael Stopa, Harvard University NNIN/C Coordinator,

21 NNIN s Computation Program (NNIN/C) Four computa8on specialists, with occasional investments in cluster- compu8ng hardware, are funded at four sites to provide access for simula8ons to experimental nanotechnology research groups Somware: a wide range of electronics/photonics/molecular dynamics Measurement of usage: complicated! consulta8on (including co- authorship), machine 8me (free except at Cornell), workshop par8cipants Research impact: > 50 in 2013, with several in high- impact journals 21

22 Adapting the NNIN to Changing User Needs User Feedback Symposia & Workshops Program Officer Review Panel NNIN Site Metrics * Engineering & Science Community Advisory Board NNIN Research Development Commercializa8on Advanced Instrumenta8on Workhorse Instrumenta8on Training Educa8on Broader Outreach Society and Ethics Students Technical Professionals Tradi8onal Fields Computa8on Emerging Fields * introduced in 2012 Major and Minor Redirec8ons Annually Emphasis, Priori8es, New Programs and Funding Alloca3ons 22

23 Lessons from NNIN A collec8on of open facili8es can have a huge benefit for for external users without strong central coordina8on NNIN funding is mostly for facili8es staff; user fees and university leverage makes up the difference Ø Tool investments: ARRA was significant, but no regular resources for significant tool acquisi8on or replacement Physical nanofabrica8on: limited inter- site usage Ø Processes and tools are usually site- specific; lack of standardiza8on Ø Contamina8on protocols are not consistent and evolve locally Ø Users typically wait for a local tool to be repaired, rather than send themselves or their wafers to other sites Computa8onal modeling program Ø Very small, but significant impact with outstanding research Ø Commercial codes: access is problema8c Ø Both local and non- local usage: requires significant hand- holding by consultants 23

24 Thoughts on Distributed Infrastructure to Accelerate MGI Staff computational scientists are more important than computational resources good ones who are committed to the mission will be hard to find Ø Newbies to computational materials will need hand-holding to become able to use codes, but must be weaned quickly or they will soak up resources and prevent scaling Ø NanoHUB.org (Purdue) has invested in user interfaces and efficient online training, which could be leveraged for MGI My own experience in DFT for work functions Ø Local NNIN/C specialist helped her get started on the open cluster Ø Her simulations overwhelmed the cluster, but helped to launch a collaboration project with Jens Nørskov and his group; his SUNCAT cluster at SLAC was critical for her Ph.D. research. 24