Improved lower and upper bounds on the tile complexity of uniquely self-assembling a thin rectangle non-cooperatively in 3D (2007.11093v1)

Abstract: We investigate a fundamental question regarding a benchmark class of shapes in one of the simplest, yet most widely utilized abstract models of algorithmic tile self-assembly. Specifically, we study the directed tile complexity of a $k \times N$ thin rectangle in Winfree's abstract Tile Assembly Model, assuming that cooperative binding cannot be enforced (temperature-1 self-assembly) and that tiles are allowed to be placed at most one step into the third dimension (just-barely 3D). While the directed tile complexities of a square and a scaled-up version of any algorithmically specified shape at temperature 1 in just-barely 3D are both asymptotically the same as they are (respectively) at temperature 2 in 2D, the bounds on the directed tile complexity of a thin rectangle at temperature 2 in 2D are not known to hold at temperature 1 in just-barely 3D. Motivated by this discrepancy, we establish new lower and upper bounds on the directed tile complexity of a thin rectangle at temperature 1 in just-barely 3D. We develop a new, more powerful type of Window Movie Lemma that lets us upper bound the number of "sufficiently similar" ways to assign glues to a set of fixed locations. Consequently, our lower bound, $\Omega\left(N^{\frac{1}{k}}\right)$, is an asymptotic improvement over the previous best lower bound and is more aesthetically pleasing since it eliminates the $k$ that used to divide $N^{\frac{1}{k}}$. The proof of our upper bound is based on a just-barely 3D, temperature-1 counter, organized according to "digit regions", which affords it roughly fifty percent more digits for the same target rectangle compared to the previous best counter. This increase in digit density results in an upper bound of $O\left(N^{\frac{1}{\left\lfloor\frac{k}{2}\right\rfloor}}+\log N\right)$, that is an asymptotic improvement over the previous best upper bound and roughly the square of our lower bound.