4 FIG. 3: (color online): (a) CNNs observed in three different 2D simulations with varying channel widths, W$_{c}$ = 1 µ m, 500 nm and 100 nm. In each case, 5 µ m length of the 20 µ m long channel is shown. For clarity, detailed view of a 1 µ m long segment of the W = 100 nm channel is also shown. Coloring scheme is based on nature of electrical transport - blue and red indicate metallic and semiconducting nanotubes, respectively. The relative number of metallic and semiconducting nanotubes is fixed at the theoretical ratio, 1:3. (b-d) Plots of the probability of the nature of electrical percolation through the network as a function of network density, ρ$_{NT}$ . The solid black, red and blue curves are probability of open circuit, semiconducting and metallic conductance across the network, respectively. The dotted lines correspond to simple stick percolation models for randomly assembled networks. (e) Contour plot of the probability of semiconducting behavior across the 2D network (shaded red) as a function of width (y-axis) and density (x-axis). The length of the channel is fixed at L = 20 µ m. (f) Same as in (e) but for multi-layer, quasi-2D simulations with varying channel thicknesses, t = 1 − 5 monolayers. The length and width of the channels are fixed, L = 5 µ m, W = 200 nm. works show a marked reduction in metallic transport as the alignment effectively shields the active components. This interplay between channel width and network den- sity is more clearly seen in Fig. 3e, a contour plot of the probability of semiconducting percolation across the channel as a function of these two variables. The network density ranges for semiconduction increases substantially as the width is decreased below W$_{c}$ ∼ 300 nm. Note that the probability of an open circuit also increases with de- creasing width, but the extent of this effect is smaller than the enhancement in semiconduction. The thickness of the CNNs permits control over out- of-plane confinement of the SWCNTs and we explore this effect by performing percolation studies on relaxed multilayered networks with fixed width and length. For computational efficiency, we have chosen networks with smaller lengths ( L$_{c}$ = 5 µ m) and channel width ( W$_{c}$ = 200 nm). Note that the network is already confined at this width. Figure 3 shows the contour plot associated with probability of semiconduction along the channel as a function of number of monolayers (ML), t = 1 − 5 ML. Our results show that thin CNNs have a higher probabil- ity for semiconduction and the probability decreases (at the expense of metallic behavior) rapidly within the first few monolayers. While we have not studied thicknesses greater than t = 5ML, the contour plot shows that the marginal decrease should be much smaller. In summary, this article demonstrates a scalable solution-based route for large-scale integrated circuits based on directed assembly of CNNs. Our results show the effectiveness of geometrical confinement induced by controlling the channel geometries, mainly their width and depth. Small, nanoscale geometries suppress the ef- fect of active elements inherent in heterogeneous SWCNT solutions, and the challenge of large-scale integration of CNN-based devices reduces to scalable, high-fidelity lithography of assembly patterns. This work was supported by the National Science Foundation Nanoscale Science and Engineering Center (NSEC) for High-rate Nanomanufacturing (NSF grant- 0425826). The experiments were conducted at the George J. Kostas Nanoscale Technology and Manufactur- ing Research Center at Northeastern University. In addi- tion, YJJ acknowledges support from nsf-cmmi 0927088 and the Fundamental R&D Program for Core Technology of Materials funded by the Ministry of Knowledge Econ- omy, Republic of Korea. HW and MU are also grate- ful for support from Structural Metallics Program, ONR (N00014-06-1-0207) and the DOE-sponsored Computa- tional Materials Science Network (CMSN). [1] P. Avouris, Z. H. Chen, and V. Perebeinos, Nat. Nan- otechnol. 2 , 605 (2007). [2] A. Javey, J. Guo, Q. Wang, M. Lundstrom, and H. Dai, Nature 424 , 654 (2003). [3] M. C. Hersam, Nat. Nanotechnol. 3 , 387 (2008). [4] E. S. Snow, J. P. Novak, M. D. Lay, E. H. Houser, F. K. Perkins, and P. M. Campbell, J. Vac. Sci. Technol. B 22 , 1990 (2004). [5] Z. Wu, Z. Chen, X. Du, J. M. Logan, J. Sippel, M. Nikolou, K. Kamaras, J. R. Reynolds, D. B. Tanner,