Encoding Light in Electronic Noise

Abstract Optical sensing in semiconductors is traditionally governed by photoconductive, photovoltaic, and photogating effects, in which optical inputs are encoded as continuous variations in average current. Here, we demonstrate a fundamentally distinct paradigm in which light is encoded in stochastic transport dynamics rather than steady-state conductance. Using a monolayer MoS2 field-effect transistor with an aggressively scaled gate stack (∼1.3 nm equivalent oxide thickness), we access a regime where interfacial defect states dominate transport and form gate-tunable potential wells. At low temperature, carrier emission from these states is suppressed, stabilizing trapped configurations in the dark, whereas optical excitation selectively activates emission, giving rise to large-amplitude (“giant”) random telegraph noise. This enables independent control of capture and emission processes through electrostatic gating and illumination, respectively, creating a direct mapping between optical input and stochastic observables such as switching rates, dwell times, and state occupancies. Interestingly, optical detection is achieved through the statistics of rare emission events, enabling sensitivity to ultralow illumination levels (∼50 fW). Spectral dependence further reveals photon energy–dependent emission kinetics, confirming the role of photon-assisted detrapping. These results show a light-induced nonequilibrium transport regime in which defect-mediated fluctuations, conventionally treated as noise, act as a functional signal and provide a stochastic sensing framework for ultralow-light detection in nanoscale electronic systems.

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Publication Details

Journal
ACS Nano
Published
2026-10-05
DOI
https://doi.org/10.1021/acsnano.6c08996
Primary Topic
2D Materials and Applications
Type
article
Field-Weighted Citation Impact
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article

Encoding Light in Electronic Noise

Saptarshi Das, Anshul Rasyotra, Kaushik Ghosh, Samriddha Ray et al.
ACS Nano
2D Materials and Applications
article

Encoding Light in Electronic Noise

Saptarshi Das, Anshul Rasyotra, Kaushik Ghosh, Samriddha Ray, Arpan Ghosh, Karthik G. Krishnan, Md Sajjad Alam, Anirban Chowdhury, Seema Rani
article en

Abstract

Abstract Optical sensing in semiconductors is traditionally governed by photoconductive, photovoltaic, and photogating effects, in which optical inputs are encoded as continuous variations in average current. Here, we demonstrate a fundamentally distinct paradigm in which light is encoded in stochastic transport dynamics rather than steady-state conductance. Using a monolayer MoS2 field-effect transistor with an aggressively scaled gate stack (∼1.3 nm equivalent oxide thickness), we access a regime where interfacial defect states dominate transport and form gate-tunable potential wells. At low temperature, carrier emission from these states is suppressed, stabilizing trapped configurations in the dark, whereas optical excitation selectively activates emission, giving rise to large-amplitude (“giant”) random telegraph noise. This enables independent control of capture and emission processes through electrostatic gating and illumination, respectively, creating a direct mapping between optical input and stochastic observables such as switching rates, dwell times, and state occupancies. Interestingly, optical detection is achieved through the statistics of rare emission events, enabling sensitivity to ultralow illumination levels (∼50 fW). Spectral dependence further reveals photon energy–dependent emission kinetics, confirming the role of photon-assisted detrapping. These results show a light-induced nonequilibrium transport regime in which defect-mediated fluctuations, conventionally treated as noise, act as a functional signal and provide a stochastic sensing framework for ultralow-light detection in nanoscale electronic systems.

ACS Nano
Pennsylvania State University (US), Center for NanoScience (DE)
Openalex Percentile: Top 26%
2D Materials and Applications
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Encoding Light in Electronic Noise — Saptarshi Das, Anshul Rasyotra, et al. · ACS Nano (2026) | TGRS Research Map | TGRS