Lithography for robust and editable atomic-scale silicon devices and memories

Abstract

At the atomic scale, there has always been a trade-off between the ease of fabrication of structures and their thermal stability. Complex structures that are created effortlessly often disorder above cryogenic conditions. Conversely, systems with high thermal stability do not generally permit the same degree of complex manipulations. Here, we report scanning tunneling microscope (STM) techniques to substantially improve automated hydrogen lithography (HL) on silicon, and to transform state-of-the-art hydrogen repassivation into an efficient, accessible error correction/editing tool relative to existing chemical and mechanical methods. These techniques are readily adapted to many STMs, together enabling fabrication of error-free, room-temperature stable structures of unprecedented size. We created two rewriteable atomic memories (1.1 petabits per in2), storing the alphabet letter-by-letter in 8 bits and a piece of music in 192 bits. With HL no longer faced with this trade-off, practical silicon-based atomic-scale devices are poised to make rapid advances towards their full potential.

Local removal of silicon layers on Si(1 0 0)-2 × 1 with chlorine-resist STM lithography

“…STM lithography on hydrogen-passivated Si(100) is one of well-developed technologies used for the fabrication of nanodevices. This approach assumes the use of hydrogen monolayer as a resist, which can be easily removed with an STM tip [1][2][3][4] . Recently, the hydrogen depassivation lithography has evolved into a wellestablished technique applied with success to the creation of a single atom transistor 5 and elements of the quantum computer 6,7 .…”

Embedding human heuristics in machine-learning-enabled probe microscopy

“…Besides its distinctive surface features, H:Si(100) is an ideal test-bed for developing CNN automation techniques. In addition to the relative simplicity of its reconstruction and a wealth of previous literature [25], H:Si(100) is a well understood substrate that has been used in many important advances in single atom technology and atomically precise materials engineering [24,[26][27][28][29][30][31]. Furthermore, because it has been previously studied in the context of machine-learning-enabled SPM [10][11][12], a good comparison can be formed with existing machine learning approaches based on full scans.…”

Detecting and Directing Single Molecule Binding Events on H-Si(100) with Application to Ultradense Data Storage

“…One limitation of these techniques, however, is that they require the tip to gather hydrogen atoms from locations outside of the fabrication area whenever the tip becomes depleted of available hydrogen. This can slow the fabrication process when many sequential corrections are required and is one of the rate-limiting factors in the rewriting speed of the atomic memory arrays 6 . Instead of bringing in external hydrogen atoms on a probe for HR we can now direct the reaction of ambient molecular hydrogen to erase DBs.…”

“…The use of DBs as bits in ultra-dense rewritable atomic memory arrays was recently demonstrated on the hydrogen-terminated silicon surface 6 . Such arrays are a promising candidate for future data storage applications due to the high barriers to diffusion for DBs along the surface, providing stability well above ambient room temperature 53,54 .…”

“…The primary rewriting mechanism of these arrays is currently HR; however, they can now be redesigned to incorporate M-HR as the main means of altering the stored information ( Supplementary Figure 8). In these new designs, each bit/DB can be converted into an IDS to be rewritten as needed, unlike in the original implementation 6 (Supplementary Figure 8a). This alteration in design reduces the maximum storage density from 1.70 bits per nm 2 to 1.36 bits per nm 2 (Supplementary Figure 8b).…”