Nano Machine Intelligence: From a Communication Perspective
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912566700146688 |
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| author | Hwang, Sangjun Koo, Bon-Hong Kim, Ho Joong Kwon, Jang-Yeon Chae, Chan-Byoung |
| author_facet | Hwang, Sangjun Koo, Bon-Hong Kim, Ho Joong Kwon, Jang-Yeon Chae, Chan-Byoung |
| contents | We present an AI-integrated molecular communication link validated on a benchtop nanomachine testbed representative of subdermal implants. The system employs an indium-gallium-zinc-oxide electrolyte-gated FET (IGZO-EGFET) functionalized with glucose oxidase as a biocompatible receiver, a microfluidic channel with a syringe-pump transmitter using on-off keying (OOK), and a machine-intelligence pipeline that addresses model mismatch and hardware non-idealities. The pipeline integrates: (i) a modular universal decoder robust to vibration-induced noise, chemical delay, and single-tap intersymbol interference; (ii) a lightweight pilot-only synchronizer that estimates symbol intervals; and (iii) a virtual-response generator that augments data and scales symbol duration. Experiments across multiple chips and sessions demonstrate end-to-end chemical text transmission with consistent error-rate reductions compared to naive thresholding and standard neural baselines. By coupling biocompatible hardware with learning-based detection and generative augmentation, this work establishes a practical route toward AI-native nanomachine networks and higher rate molecular links, while providing a system blueprint adaptable to other biochemical modalities. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_02235 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nano Machine Intelligence: From a Communication Perspective Hwang, Sangjun Koo, Bon-Hong Kim, Ho Joong Kwon, Jang-Yeon Chae, Chan-Byoung Systems and Control 94A05 C.2.1 We present an AI-integrated molecular communication link validated on a benchtop nanomachine testbed representative of subdermal implants. The system employs an indium-gallium-zinc-oxide electrolyte-gated FET (IGZO-EGFET) functionalized with glucose oxidase as a biocompatible receiver, a microfluidic channel with a syringe-pump transmitter using on-off keying (OOK), and a machine-intelligence pipeline that addresses model mismatch and hardware non-idealities. The pipeline integrates: (i) a modular universal decoder robust to vibration-induced noise, chemical delay, and single-tap intersymbol interference; (ii) a lightweight pilot-only synchronizer that estimates symbol intervals; and (iii) a virtual-response generator that augments data and scales symbol duration. Experiments across multiple chips and sessions demonstrate end-to-end chemical text transmission with consistent error-rate reductions compared to naive thresholding and standard neural baselines. By coupling biocompatible hardware with learning-based detection and generative augmentation, this work establishes a practical route toward AI-native nanomachine networks and higher rate molecular links, while providing a system blueprint adaptable to other biochemical modalities. |
| title | Nano Machine Intelligence: From a Communication Perspective |
| topic | Systems and Control 94A05 C.2.1 |
| url | https://arxiv.org/abs/2509.02235 |