The growing importance of applications based on machine learning is driving the need to develop dedicated, energy-efficient electronic hardware. Compared with von-Neumann architectures, brain-inspired in-memory computing uses the same basic device structure for logic operations and data storage 1 – 3 , thus promising to reduce the energy cost of data-centric computing significantly 4 . While there is ample research focused on exploring new device architectures, the engineering of material platforms suitable for such device designs remains a challenge. Two-dimensional materials 5 , 6 such as semiconducting MoS2 could stand out as a promising candidate to face this obstacle thanks to their exceptional electrical and mechanical properties 7 – 9 . Here, we explore large-area grown MoS2 as an active channel material for developing logic-in-memory devices and circuits based on floating-gate field-effect transistors (FGFET). The conductance of our FGFETs can be precisely and continuously tuned, allowing us to use them as building blocks for reconfigurable logic circuits where logic operations can be directly performed using the memory elements. After demonstrating a programmable NOR gate, we show that this design can be simply extended to implement more complex programmable logic and functionally complete sets of functions. Our findings highlight the potential of atomically thin semiconductors for the development of next-generation low-power electronics.

The direct manipulation of individual atoms in materials using scanning probe microscopy has been a seminal achievement of nanotechnology. Recent advances in imaging resolution and sample stability have made scanning transmission electron microscopy a promising alternative for single-atom manipulation of covalently bound materials. Pioneering experiments using an atomically focused electron beam have demonstrated the directed movement of silicon atoms over a handful of sites within the graphene lattice. Here, we achieve a much greater degree of control, allowing us to precisely move silicon impurities along an extended path, circulating a single hexagon, or back and forth between the two graphene sublattices. Even with manual operation, our manipulation rate is already comparable to the state-of-the-art in any atomically precise technique. We further explore the influence of electron energy on the manipulation rate, supported by improved theoretical modeling taking into account the vibrations of atoms near the impurities, and implement feedback to detect manipulation events in real time. In addition to atomic-level engineering of its structure and properties, graphene also provides an excellent platform for refining the accuracy of quantitative models and for the development of automated manipulation.

Incorporating heteroatoms into the graphene lattice may be used to tailor its electronic, mechanical and chemical properties, although directly observed substitutions have thus far been limited to incidental Si impurities and P, N and B dopants introduced using low-energy ion implantation. We present here the heaviest impurity to date, namely Ge ions implanted into monolayer graphene. Although sample contamination remains an issue, atomic resolution scanning transmission electron microscopy imaging and quantitative image simulations show that Ge can either directly substitute single atoms, bonding to three carbon neighbors in a buckled out-of-plane configuration, or occupy an in-plane position in a divacancy. First-principles molecular dynamics provides further atomistic insight into the implantation process, revealing a strong chemical effect that enables implantation below the graphene displacement threshold energy. Our results demonstrate that heavy atoms can be implanted into the graphene lattice, pointing a way toward advanced applications such as single-atom catalysis with graphene as the template.

Atomic engineering is envisioned to involve selectively inducing the desired dynamics of single atoms and combining these steps for larger-scale assemblies. Here, we focus on the first part by surveying the single-step dynamics of graphene dopants, primarily phosphorus, caused by electron irradiation both in experiment and simulation, and develop a theory for describing the probabilities of competing configurational outcomes depending on the postcollision momentum vector of the primary knock-on atom. The predicted branching ratio of configurational transformations agrees well with our atomically resolved experiments. This suggests a way for biasing the dynamics toward desired outcomes, paving the road for designing and further upscaling atomic engineering using electron irradiation.

Surface impurities and contamination often seriously degrade the properties of two‐dimensional materials such as graphene. To remove contamination, thermal annealing is commonly used. We present a comparative analysis of annealing treatments in air and in vacuum, both ex situ and “pre situ,” where an ultra‐high vacuum treatment chamber is directly connected to an aberration‐corrected scanning transmission electron microscope. While ex situ treatments do remove contamination, it is challenging to obtain atomically clean surfaces after ambient transfer. However, pre situ cleaning with radiative or laser heating appears reliable and well suited to clean graphene without damage to most suspended areas. Pre situ annealing of typical dirty graphene samples yields atomically clean areas several hundred nm2 in size.