Characterization of Electronic Stress-Induced Changes in Multi-Layer MoS2

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Abstract

Transition metal dichalcogenides like molybdenum disulfide (MoS2) are compelling for next-generation electronic devices. In this work, we investigate the impact of electronic stress on MoS2 to illustrate that observational and phenomenological information on multiple devices can be useful to describe changes in the system, and to caution against the rationaliza-tion of paltry results as representative, or correlative, to device behavior. Here, we stress MoS2 by applying a sustained 20 V DC bias to study the material's response. Post-stress electronic characterization revealed nonuniform shifts in current-voltage (i-V) behavior alongside microscale changes. Complementary mechanical, spectroscopic, and scanning microwave impedance measurements showed that stress-induced features locally modulate stiffness, surface potential, Raman intensity, and charge carrier density. We correlated i-V behavior with mor-phological features (wrinkles, tears, folds, height), device-level geometry (MoS2 overlap with electrodes, channel area, contact length) on 50 test structures across five chips to move beyond anecdotal conclusions. We found no universal correlations be-fore DC stress; all trends were explained by chip dependent performance. However, device-level geometry correlated with i-V behavior after DC stress, suggesting electrode contact domi-nates over morphology as a driver of performance. Morphologi-cal changes after DC stress, such as delamination and material thinning, result in reduced charge carrier density. Delamination and thinning appear to map to i-V device performance in a few samples, but the correlation is lost when a larger sample size is considered. This suggests significant sample-to-sample varia-bility in surface electronic states of the test structures. We also discuss the potential of environmental impacts from processing and test approach that could influence heterogenous response. Progress will require high-resolution, multimodal analysis across many samples constructed under controlled, clean condi-tions. By building datasets that capture variability, we can bet-ter identify the true drivers of performance.

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Materials characterization and Electronics

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