Publication date: 24th July 2026
Neuromorphic computing based on memristors and memtransistors has gained increasing interest, with two-dimensional (2D) molybdenum ditelluride (MoTe₂) emerging as a promising material due to its low phase-transition energy barrier and tunable electrical properties. Despite these advantages, the relatively narrow memory window of pristine MoTe₂ devices has limited their analog switching performance and neuromorphic functionality. In this talk, I demonstrate a post-processing strategy to enhance the analog switching behavior of lateral MoTe₂ devices through two sequential steps: laser treatment followed by atomic layer deposition of Al₂O₃. This combined approach led to a 70-fold increase in dynamic range in the memristor mode and a 20-fold increase in the memtransistor mode. As a result, artificial neural network simulations exhibited a 10-fold improvement in pattern recognition accuracy. Furthermore, the ability of the devices to operate as both memristors and memtransistors enabled the emulation of both homosynaptic and heterosynaptic plasticity, highlighting their potential for neuromorphic computing applications.
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