Stimulus‐Gated Neuromorphic Watermarking With 2D Siloxene‐Based UV Synaptic Phototransistors for Next‐Generation IC Security
Counterfeiting of integrated circuits (ICs) and intellectual property (IP) infringement pose increasing threats to modern electronics. Hardware watermarking is a key technology for anti-counterfeiting and IP protection. However, existing algorithmic and memory-based watermarking schemes are static, lack activation-level security, are inflexible, and are restricted only to IP protection. Herein, a groundbreaking stimulus-gated neuromorphic watermarking strategy for hardware security is demonstrated using UV-triggered synaptic phototransistors based on two-dimensional (2D) siloxene nanosheets. By emulating biological synaptic behavior, a dynamic watermark is designed that can only be unlocked via application of UV light pulses possessing precisely defined parameters of intensity, pulse duration, and pulse interval, thereby providing multi-layered activation-dependent security. These parameters are retained as manufacturer secrets, eliminating chances of reverse engineering and duplication of the watermark onto fake ICs. A deterministic excitatory post-synaptic current (EPSC)-stimulus model is developed that quantitatively links the optical input to synaptic current evolution, enabling reproducible logic-state transitions. Additionally, the flexible transistor array architecture permits integration of the watermark into wearable electronics. This work establishes an experimentally validated neuromorphic-based security paradigm in the time domain with stimulus-gated concealment, providing a watermarking scheme that functions both as an ownership identifier and as a practical anti-counterfeiting primitive for next-generation secure electronics.
Authors
- Riya Sadhukhan
- Rajat Banerjee (ORCID: https://orcid.org/0000-0002-8001-7210)
- Subhrakanti Dey (ORCID: https://orcid.org/0000-0003-0762-5743)
- D. K. Goswami (ORCID: https://orcid.org/0000-0002-5891-6172)
- Priyanka Rani (ORCID: https://orcid.org/0000-0002-9639-8513)
- Samik Mallik
- Madhuchanda Banerjee
- Asima Pradhan
Institutions
- Indian Institute of Technology Kharagpur (IN)
- Midnapore Medical College and Hospital (IN)
Publication Details
- Journal
- Small
- Published
- 2026-01-14
- DOI
- https://doi.org/10.1002/smll.202509193
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00