The Commercialization of Nanotechnology
Nanotech is often defined as the manipulation and control of matter at the nanometer scale (critical dimensions of 1 to 100nm). It is a bit unusual to describe a technology by a length scale. We certainly didn’t get very excited by “inch-o technology.” As venture capitalists, we start to get interested when there are unique properties of matter that emerge at the nanoscale and that cannot be exploited at the macroscale world of today’s engineered products. We like to ask the start-ups that we are investing in, “Why now? Why couldn’t you have started this business ten years ago?” The responses of our nanotech start-ups have a common thread: Recent developments in the capacity to understand and engineer nanoscale materials have enabled new products that could not have been developed at larger scale.
Various unique properties of matter are expressed at the nanoscale and are quite foreign to our “bulk statistical” senses (we do not see single photons or quanta of electric charge; we feel bulk phenomena, like friction, at the statistical or emergent macroscale). At the nanoscale, the bulk approximations of Newtonian physics are revealed for their inaccuracy and give way to quantum physics. Nanotechnology is more than a linear improvement with scale; everything changes. Quantum entanglement, tunneling, ballistic transport, frictionless rotation of superfluids, and several other phenomena have been regarded as “spooky” by many of the smartest scientists, even Einstein, upon first exposure.
For a simple example of nanotech’s discontinuous divergence from the “bulk” sciences, consider the simple aluminum soda can. If you take the inert aluminum metal in that can and grind it down into a powder of 2030nm particles, it will spontaneously explode in air. It becomes a rocket fuel catalyst. In other words, the energetic properties of matter change at that scale. The surface-area-to-volume ratios become relevant, and even the distances between the atoms in a metal lattice change from surface effects.
Innovation from the Edge
Disruptive innovation, the driver of growth and renewal, occurs at the edge. In start-ups, innovation occurs out of the mainstream, away from the warmth of the herd. In biological evolution, innovative mutations take hold at the physical edge of the population, at the edge of survival. In complexity theory, structure and complexity emerge at the edge of chaosthe dividing line between predictable regularity and chaotic indeterminacy. And in science, meaningful disruptive innovation occurs at the interdisciplinary interstices between formal academic disciplines.
Herein lies much of the excitement about nanotechnology: in the richness of human communication about science. Nanotech exposes the core areas of overlap in the fundamental sciences, the place where quantum physics and quantum chemistry can cross-pollinate with ideas from the life sciences.
In academic centers and government laboratories, nanotech is fostering new discussions. At Stanford, UCLA, Duke, and many other schools, the new nanotech buildings are physically located at the symbolic hub of the schools of engineering, computer science, and medicine.
Nanotech is the nexus of the sciences, but outside the sciences and research itself, the nanotech umbrella conveys no business synergy whatsoever. The marketing, distribution, and sales of a nanotech solar cell, memory chip, or drug delivery capsule will be completely different from each other and will present few opportunities for common learning or synergy.
Market Timing
As an umbrella term for a myriad of technologies spanning multiple industries, nanotech will eventually disrupt these industries over different time framesbut most are long-term opportunities. Electronics, energy, drug delivery, and materials are areas of active nanotech research today. Medicine and bulk manufacturing are future opportunities. The National Science Foundation predicts that nanotech will have a trillion-dollar impact on various industries within 15 years.
Of course, if one thinks far enough in the future, every industry eventually will be revolutionized by a fundamental capability for molecular manufacturingfrom the inorganic structures to the organic and even the biological. Analog manufacturing will become digital, engendering a profound restructuring of the substrate of the physical world.
Futuristic predictions of potential nanotech products have a near-term benefit. They help attract some of the best and brightest scientists to work on hard problems that are stepping-stones to the future vision. Scientists relish exploring the frontier of the unknown, and nanotech embodies the tangible metaphor of the inner frontier.
Given that much of the abstract potential of nanotech is a question of “when” and not “if,” the challenge for the venture capitalist is one of market timing. When should we be investing, and in which subsectors? It is as if we need to pull the sea of possibilities through an intellectual filter to tease apart the various segments into a time line of probable progression. That is an ongoing process of data collection (for example, the growing pool of business plan submissions), business and technology analysis, and intuition.
Two touchstone events for the scientific enthusiasm for the timing of nanotech were the decoding of the human genome and the dazzling visual images output by the scanning tunneling microscope (such as the arrangement of individual xenon atoms into the IBM logo). These events represent the digitization of biology and mattersymbolic milestones for accelerated learning and simulation-driven innovation.
More recently, nanotech publication has proliferated, as in the early days of the Internet. In addition to the popular press, the number of scientific publications on nanotech has grown by a factor of 10 in the past ten years. According to the U.S. Patent and Trademark Office (USPTO), the number of nanotech patents granted each year has skyrocketed by a factor of 3 in the past seven years. Ripe with symbolism, IBM has more lawyers working on nanotech than engineers.
With the recent codification of the National Nanotech Initiative into law, federal funding will continue to fill the pipeline of nanotech research. With $847 million earmarked for 2004, nanotech was a rarity in the tight budget process; it received more funding than was requested. Now nanotech is second only to the space race for federal funding of science. And the United States is not alone in funding nanotechnology. Unlike many previous technological areas, we aren’t even in the lead; Japan outspends the United States each year on nanotech research. In 2003, the U.S. government spending was one-fourth of the world total.
Federal funding is the seed corn for nanotech entrepreneurship. All of our nanotech portfolio companies are spin-offs (with negotiated intellectual property [IP] transfers) from universities or government labs, and all got their start with federal funding. Often these companies need specialized equipment and expensive laboratories to do the early tinkering that will germinate a new breakthrough. These are typically lacking in the proverbial entrepreneur’s garage.
Corporate investors have discovered a keen interest in nanotechnology, with internal R&D, external investments in start-ups, and acquisitions of promising companies, such as chipmaker AMD’s recent acquisition of Coatue, a molecular electronics company.
Despite all this excitement, there are a fair number of investment dead ends, and so we continue to refine the filters we use in selecting companies to back. All entrepreneurs want to present their businesses as fitting an appropriate time line to commercialization. How can we guide our intuition to determine which of these entrepreneurs are right?
The Question of Vertical Integration
Nanotech involves the reengineering of the lowest-level physical layer of a system, and so a natural business question arises: How far forward do you need to vertically integrate before you can sell a product on the open market? For example, in molecular electronics, if you can ship a DRAM-compatible chip, you have found a horizontal layer of standardization, and further vertical integration is not necessary. If you have an incompatible 3-D memory block, you may have to vertically integrate to the storage subsystem level, or farther, to bring a product to market. That may require that you form industry partnerships, and it will, in general, take more time and money as change is introduced farther up the product stack. Three-dimensional logic with massive interconnectivity may require a new computer design and a new form of software; this would take the longest to commercialize. And most start-ups on this end of the spectrum would seek partnerships to bring their vision to market. The success and timeliness of that endeavor will depend on many factors, including IP protection, the magnitude of improvement, the vertical tier at which that value is recognized, the number of potential partners, and the needed degree of tooling and other industry accommodations.
Product development time lines are impacted by the cycle time of the R&D feedback loop. For example, outdoor lifetime testing for organic light-emitting diodes (LEDs) will take longer than in silicon simulation spins of digital products. If the product requires partners in the R&D loop or multiple nested tiers of testing, it will take longer to commercialize.
The Interface Problem
As we think about the start-up opportunities in nanotechnology, an uncertain financial environment underscores the importance of market timing and revenue opportunities over the next five years. Of the various paths to nanotech, which of them are 20-year quests in search of a government grant, and which are market-driven businesses that will attract venture capital? Are there co-factors of production that require a whole industry to be in place before a company ships products?
As a thought experiment, imagine that I could hand you today any nanotech marvel of your designa molecular machine as advanced as you would like. What would it be? A supercomputer? A bloodstream submarine? A matter compiler capable of producing diamond rods or arbitrary physical objects? Pick something.
Now imagine some of the complexities: Did it blow off my hand as I offered it to you? Can it autonomously move to its intended destination? What is its energy source? How do you communicate with it?
These questions draw the interface problem into sharp focus: Does your design require an entire nanotech industry to support, power, and interface to your molecular machine? As an analogy, imagine that you have one of the latest Intel Pentium processors. How would you make use of the Pentium chip? You then need to wire-bond the chip to a larger lead frame in a package that connects to a larger printed circuit board, fed by a bulky power supply that connects to the electrical power grid. Each of these successive layers relies on its larger-scale precursors (which were developed in reverse chronological order), and the entire hierarchy is needed to access the potential of the microchip.
Where Is the Scaling Hierarchy for Molecular Nanotech?
To cross the interface chasm, today’s business-driven paths to nanotech diverge into two strategies: the biologically inspired bottom-up path, and the top-down approach of the semiconductor industry. The developers of nonbiological micro-electromechanical systems (MEMS) are addressing current markets in the micro world while pursuing an ever-shrinking spiral of miniaturization that builds the relevant infrastructure tiers along the way. Not surprisingly, this path is very similar to the one that has been followed in the semiconductor industry, and many of its adherents see nanotech as inevitable but in the distant future.
On the other hand, biological manipulation presents numerous opportunities to effect great change in the near term. Drug development, tissue engineering, and genetic engineering are all powerfully impacted by the molecular manipulation capabilities available to us today. And genetically modified microbes, whether by artificial evolution or directed gene splicing, give researchers the ability to build structures from the bottom up.
The Top-Down “Chip Path”
This path is consonant with the original vision of physicist Richard Feynman (in a 1959 lecture at Caltech) of the iterative miniaturization of our tools down to the nanoscale. Some companies are pursuing the gradual shrinking of semiconductor manufacturing technology from the MEMS of today into the nanometer domain of nanoelectromechanical systems (NEMS).
MEMS technologies have already revolutionized the automotive industry with air-bag sensors, and the printing sector with ink-jet nozzles, and they are on track to do the same in medical devices and photonic switches for communications and mobile phones. In-StatJMDR forecasts that the $4.7 billion in MEMS revenue in 2003 will grow to $8.3 billion by 2007. But progress is constrained by the pace (and cost) of the semiconductor equipment industry, and by the long turnaround time for fab runs.
Many of the nanotech advances in storage, semiconductors, and molecular electronics can be improved, or in some cases enabled, by tools that allow for the manipulation of matter at the nanoscale. Here are three examples:
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Nanolithography: Molecular Imprints is commercializing a unique imprint lithographic technology developed at the University of Texas at Austin. The technology uses photo-curable liquids and etched quartz plates to dramatically reduce the cost of nanoscale lithography. This lithography approach, recently added to the ITRS Roadmap, has special advantages for applications in the areas of nanodevices, MEMS, microfluidics, and optical components and devices, as well as molecular electronics.
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Optical traps: Arryx has developed a breakthrough in nanomaterial manipulation. Optical traps generate hundreds of independently controllable laser tweezers that can manipulate molecular objects in 3-D (move, rotate, cut, place), all from one laser source passing through an adaptive hologram. The applications span from cell sorting, to carbon nanotube placement, to continuous material handling. They can even manipulate the organelles inside an unruptured living cell (and weigh the DNA in the nucleus).
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Metrology: Imago’s LEAP atom probe microscope is being used by the chip and disk drive industries to produce 3-D pictures that depict both the chemistry and the structure of items on an atom-by-atom basis. Unlike traditional microscopes, which zoom in to see an item on a microscopic level, Imago’s nanoscope analyzes structures, one atom at a time, and “zooms out” as it digitally reconstructs the item of interest at a rate of millions of atoms per minute. This creates an unprecedented level of visibility and information at the atomic level.
Advances in nanoscale tools help us control and analyze matter more precisely, which in turn allows us to produce better tools. To summarize, the top-down path is designed and engineered with the following:
The Biological, Bottom-Up Path
In contrast to the top-down path, the biological bottom-up archetype is
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Grown via replication, evolution, and self-assembly in a 3-D, fluid medium
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Constrained at interfaces to the inorganic world
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Limited by gaps in learning and theory (in systems biology, complexity theory, and the pruning rules of emergence)
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Bootstrapped by a powerful preexisting hierarchy of interpreters of digital molecular code
To elaborate on this last point, a ribosome takes digital instructions in the form of mRNA and manufactures almost everything we care about in our bodies from a sequential concatenation of amino acids into proteins. The ribosome is a wonderful existence proof of the power and robustness of a molecular machine. It is roughly 20nm on a side and consists of only 99,000 atoms. Biological systems are replicating machines that parse molecular code (DNA) and a variety of feedback to grow macroscale beings. These highly evolved systems can be hijacked and reprogrammed to great effect.
So how does this help with the development of molecular electronics or nanotech manufacturing? The biological bootstrap provides a more immediate path to nanotech futures. Biology provides us with a library of prebuilt components and subsystems that can be repurposed and reused, and research in various labs is well under way in reengineering the information systems of biology.
For example, researchers at NASA’s Ames Research Center are taking self-assembling heat shock proteins from thermophiles and genetically modifying them so that they will deposit a regular array of electrodes with a 17nm spacing. This could be useful for making patterned magnetic media in the disk drive industry or electrodes in a polymer solar cell.
At MIT, researchers are using accelerated artificial evolution to rapidly breed an Ml3 bacteriophage to infect bacteria in such a way that they bind and organize semiconducting materials with molecular precision.
At the Institute for Biological Energy Alternatives (IBEA), Craig Venter and Hamilton Smith are leading the Minimal Genome Project. They take Mycoplasma genitalium from the human urogenital tract and strip out 200 unnecessary genes, thereby creating the simplest organism that can self-replicate. Then they plan to layer new functionality onto this artificial genome, such as the ability to generate hydrogen from water using the sun’s energy for photonic hydrolysis.
The limiting factor is our understanding of these complex systems, but our pace of learning has been compounding exponentially. We will learn more about genetics and the origins of disease in the next ten years than we have in all of human history. And for the minimal genome microbes, the possibility of understanding the entire proteome and metabolic pathways seems tantalizingly close to achievable. These simpler organisms have a simple “one gene, one protein” mapping and lack the nested loops of feedback that make the human genetic code so rich.
An Example: Hybrid Molecular Electronics
In the near term, a variety of companies are leveraging the power of organic self-assembly (bottom-up) and the market interface advantages of top-down design. The top-down substrate constrains the domain of self-assembly.
Based in Denver, ZettaCore builds molecular memories from energetically elegant molecules that are similar to chlorophyll. ZettaCore’s synthetic organic porphyrin molecule self-assembles on exposed silicon. These molecules, called multiporphyrin nanostructures, can be oxidized and reduced (their electrons removed or replaced) in a way that is stable, reproducible, and reversible. In this way, the molecules can be used as a reliable storage medium for electronic devices.
Furthermore, the molecules can be engineered to store multiple bits of information and to maintain that information for relatively long periods before needing to be refreshed. Recall the water-drop-to-transistor-count comparison, and add to that the fact that these multiporphyrins have already demonstrated as many as eight stable digital states per molecule.
The technology has future potential to scale to 3-D circuits with minimal power dissipation, but initially it will enhance the weakest element of an otherwise standard 2-D memory chip. To end customers, the ZettaCore memory chip looks like a standard memory chip; nobody needs to know that it has “nano inside.” The input/output pads, sense amps, row decoders, and wiring interconnect are produced via a standard semiconductor process. As a final manufacturing step, the molecules are splashed on the wafer, where they self-assemble in the predefined regions of exposed metal.
From a business perspective, this hybrid product design allows an immediate market entry because the memory chip defines a standard product feature set, and the molecular electronics manufacturing process need not change any of the prior manufacturing steps. Any interdependencies with the standard silicon manufacturing steps are also avoided, thanks to this late coupling; the fab can process wafers as it does now before spin-coating the molecules. In contrast, new materials for gate oxides or metal interconnects can have a number of effects on other processing steps, and these effects need to be tested. That introduces delay (as with copper interconnects).
Generalizing from the ZettaCore experience, the early revenue in molecular electronics will likely come from simple 1-D structures such as chemical sensors and self-assembled 2-D arrays on standard substrates, such as memory chips, sensor arrays, displays, CCDs for cameras, and solar cells.
IP and Business Model
Beyond product development time lines, the path to commercialization is dramatically impacted by the cost and scale of the manufacturing ramp. Partnerships with industry incumbents can be an accelerant or an albatross for market entry.
The strength of the IP protection for nanotech relates to the business models that can be safely pursued. For example, if the composition of matter patents afford the nanotech start-up the same degree of protection as for a biotech start-up, then a “biotech licensing model” may be possible in nanotech. A molecular electronics company could partner with a large semiconductor company for manufacturing, sales, and marketing, just as a biotech company partners with a big pharmaceutical partner for clinical trials, marketing, sales, and distribution. In both cases, the cost to the big partner is on the order of $100 million, and the start-up earns a royalty on future product sales.
Notice how the transaction costs and viability of this business model option pivot on the strength of IP protection. A software business, on the other end of the IP spectrum, would be very cautious about sharing its source code with Microsoft in the hopes of forming a partnership based on royalties.
Manufacturing partnerships are common in the semiconductor industry, with the “fabless” business model. This layering of the value chain separates the formerly integrated functions of product conceptualization, design, manufacturing, testing, and packaging. This has happened in the semiconductor industry because the capital cost of manufacturing is so large. The fabless model is a useful way for a small company with a good idea to bring its own product to market, but the company then must face the issue of gaining access to its market and funding the development of marketing, distribution, and sales.
Having looked at the molecular electronics example in some depth, we can now move up the abstraction ladder to aggregates, complex systems, and the potential to advance the capabilities of Moore’s Law in software.
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