have evolved to optimize steps other than just catalysis (such as the binding of substrates and the release of products), the model used by the authors 1,2 to design their enzymes doesn’t attempt to address these factors. This is under- standable, because many of the finer features that provide enzymes with their unique prop- erties are not yet understood. For example, the mutations introduced by the authors into their enzymes by directed evolution did not modify the active site itself, but occurred at neighbour- ing positions (Fig. 1). The effect of some of these mutations can be easily understood with hindsight, but others are much less obvious. It was therefore wise of the authors to let nature lend a helping hand in their designs. Nevertheless, these results 1,2 are a milestone in biochemistry. For the first time, artificial enzymes have been designed for non-biological reactions, providing rate accelerations that are about 1,000 times faster than previous exam- ples of computationally designed enzymes. Biochemists have long wanted to build artifi- cial enzymes to identify and validate the mini- mal requirements for enzyme-like catalysis. These reports provide an accurate framework for this enterprise to which further features can be added. As Röthlisberger et al. 1 note, the abil- ity to design enzymes will truly test our under- standing of enzyme catalysis. ■ Giovanna Ghirlanda is in the Department of Chemistry and Biochemistry, Arizona State University, Tempe, Arizona 85287-1604, USA. e-mail: giovanna.ghirlanda@asu.edu DEVICE PHYSICS Chance match Robert M. Westervelt A clever device uses the quantum statistics of electron tunnelling to match image patterns. The circuit is low-power, works at room temperature — and could point to a way forward for silicon electronics. uses: extracting a simple conclusion from a great body of input data, for instance. Nishiguchi et al. 4 build on previous work 6–8 to construct a simple pattern-matching circuit using a basic building-block of two transistors (more precisely, metal–oxide–semiconduc- tor field-effect transistors, or MOSFETs) pat- terned on a silicon-on-insulator wafer. They trap and store single electrons on the first of these nanoscale transistors, the ‘T-FET’. They are able to reduce the rate at which electrons tunnel quantum-mechanically into a storage node on the T-FET to very low levels of around one per second. The authors show that the trapped electrons obey Poisson statistics, and represent a statistically random source of events that can be used for stochastic signal processing 9 . The job of the second transistor that makes up the authors’ processor, the ‘D-FET’, is to detect the number of electrons stored in the T-FET. It does this through a capacitative coup- ling: as the number of electrons stored in the T-FET increases, the current passing through the D-FET is progressively reduced. The coup- ling is sensitive enough that the tunnelling of a single electron into the T-FET is registered as a discrete drop in current in the D-FET. To perform pattern matching, the individual bits of an input image must be compared with those of a reference image. Nishiguchi and colleagues set an input bit, i, to 0 or 1 by stepping the input ‘source’ volt- age of the T-FET. Similarly, they set a reference bit, r, by changing the T-FET’s ‘gate’ voltage, which con- trols the passage of current from the source into the storage node (Fig. 1). When i = 0, the tunnelling rate into the T-FET is negligible. When i = 1, tunnelling occurs, and the number of electrons stored in the T-FET slowly builds up. The precise rate of this tunnelling is controlled by the gate voltage, and thus the reference bit: it is large when r = 1, and small when r = 0. Essentially, this set-up creates a detector that flags up when the input and reference bits are both on, i = r = 1: in this case, electrons build up in the T-FET particularly r = 1 i = 1 ? r = 0 ? i = 0 D-FET T-FET Capacitance gap Depleted current Gate Source Stored electrons Summed current 1. Röthlisberger, D. et al. Nature 453, 190–195 (2008). 2. Jiang, L. et al. Science 319, 1387–1391 (2008). 3. Dahiyat, B. I. & Mayo, S. L. Science 278, 82–87 (1997). 4. Kuhlman, B. et al. Science 302, 1364–1368 (2003). 5. Looger, L. L., Dwyer, M. A., Smith, J. J. & Hellinga, H. W. Nature 423, 185–190 (2003). 6. Bolon, D. N. & Mayo, S. L. Proc. Natl Acad. Sci. USA 98, 14274–14279 (2001). 7. Kaplan, J. & DeGrado, W. F. Proc. Natl Acad. Sci. USA 101, 11566–11570 (2004). 8. Geremia, S. et al. J. Am. Chem. Soc. 127, 17266–17276 (2005). 9. http://boinc.bakerlab.org/rosetta/ 10. Sinha, S. C., Barbas, C. F. & Lerner, R. A. Proc. Natl Acad. Sci. USA 95, 14603–14608 (1998). 11. Tanaka, F. & Barbas, C. F. Chem. Commun. 769–770 (2001). 12. Tanaka, F., Fuller, R. & Barbas, C. F. Biochemistry 44, 7583–7592 (2005). 13. Hu, Y., Houk, K. N., Kikuchi, K., Hotta, K. & Hilvert, D. J. Am. Chem. Soc. 126, 8197–8205 (2004). 14. Thorn, S. N., Daniels, R. G., Auditor, M.-T. M. & Hilvert, D. Nature 373, 228–230 (1995). Over the past three decades, as the components that make up integrated circuits have been made smaller and smaller, the power of com- puter chips has grown exponentially, even as their cost has fallen drastically. But sooner rather than later — by around 2020, according to one estimate 1 — the scaling-down process will become difficult to maintain 2,3 . The energy required to represent a bit of information will become larger than the heat that can be car- ried away from a tiny circuit element; what’s more, as devices approach the size of atoms, quantum-physical phenomena will become important, changing even the ground rules of how bits are processed. Writing in Applied Physics Letters 4 , Nishiguchi et al. detail what might be one way to circumvent, and even exploit, these issues. They describe a circuit that allows them to perform the comput- ing operation of pattern matching by harnessing the stochastic, quantum- mechanical tunnelling of single elec- trons into a transistor 5 . Pattern recognition is a natural enough task for people, but is often difficult for computers. We would like our computer processors to be like us and recognize an object (a cat or a dog, say) in a photographic image, under- stand the meaning of spoken words, or translate efficiently from one language to another. But pattern recognition also has more abstract, fundamental Figure 1 | Dual processor. Nishiguchi and colleagues’ pattern-recognition processor 4 uses two basic components that each consist of two capacitatively coupled transistors: a transfer transistor (T-FET) and a detector transistor (D-FET). The probability that an electron will tunnel from the source of the T-FET, under the gate and into the storage node is determined by the source voltage, which is set by the value of a bit i in the input image, and by the gate voltage, which is set by a bit r in the reference image. The more electrons accumulate in the T-FET storage node, the lower the current that flows through the capacitatively coupled D-FET. In the instance depicted, both the input and reference bits are turned on, i = r = 1, and electrons accumulate in the storage node, reducing the detector current. The second unit (right) is fed with the inverse inputs of the first, ¯ i and ¯ r. If the original inputs were matched at 0, the inputs here would be 1, and this half of the processor would record the depleted current characteristic of matched bits. (Figure adapted from ref. 1.) 166 NATURE|Vol 453|8 May 2008 NEWS & VIEWS