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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, which have separate processing and storage units, brain-inspired in-memory computing uses the same basic device structure for logic operations and data storage^{, –}, thus promising to reduce the energy cost of data-centred computing substantially. Although 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^, such as semiconducting molybdenum disulphide, MoS₂, could be promising candidates for such platforms thanks to their exceptional electrical and mechanical properties^{, –}. Here we report our exploration of large-area MoS₂ as an active channel material for developing logic-in …