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High-density neural interfacesare central to next-generation brain-machine interfaces (BMIs),neuroprosthetics, and large-scale neuroscience research. Modern neural probesintegrate hundreds to thousands of recording channels, creating stringentrequirements on power consumption, data bandwidth, and scalability.Conventional Nyquist-rate ADCs digitize neural signals continuously, resultingin redundant data, excessive power dissipation, and bandwidth bottlenecks -especially given the sparse, event-based nature of neural activity.
Event-driven (or asynchronous)signal acquisition architectures offer a promising alternative. By digitizingneural signals only when meaningful activity occurs (e.g., spikes orsignificant local field potential changes), event-driven ADCs can drasticallyreduce power consumption and data throughput while preserving critical neuralinformation. This approach aligns naturally with high-density neural recordingsystems, where per-channel power budgets are extremely limited.
This internship proposes thedesign and evaluation of an event-driven ADC architecture optimized forhigh-density neural interfaces, targeting ultra-low power operation,scalability, and compatibility with advanced neural probes.
Required skills:
Required background: Major in electricalengineering or related.
Type of work: 20% literature review,20% architecture definition and modelling, 60% circuit innovation (analog ICdesign)
Supervisor: Chris Van Hoof
Daily advisor: Xiaolin Yang
Type of internship: Master internship, PhD internship
Duration: 6-12 months
Required educational background: Electrotechnics/Electrical Engineering
University promotor: Chris Van Hoof (KU Leuven)
Supervising scientist(s): For further information or for application, please contact Xiaolin Yang ([email protected])
The reference code for this position is 2026-INT-068. Mention this reference code in your application.
Imec allowance will be provided.
Applications should include the following information:
Incomplete applications will not be considered.
Event-driven (or asynchronous)signal acquisition architectures offer a promising alternative. By digitizingneural signals only when meaningful activity occurs (e.g., spikes orsignificant local field potential changes), event-driven ADCs can drasticallyreduce power consumption and data throughput while preserving critical neuralinformation. This approach aligns naturally with high-density neural recordingsystems, where per-channel power budgets are extremely limited.
This internship proposes thedesign and evaluation of an event-driven ADC architecture optimized forhigh-density neural interfaces, targeting ultra-low power operation,scalability, and compatibility with advanced neural probes.
Required skills:
- Strong background in analog and mixed-signal circuit design.
- Good knowledge of Cadence environment for schematic entry, simulations and custom layout
- Strong problem-solving skills.
- Eagerness to learn and innovate.
- Good communication skills.
Required background: Major in electricalengineering or related.
Type of work: 20% literature review,20% architecture definition and modelling, 60% circuit innovation (analog ICdesign)
Supervisor: Chris Van Hoof
Daily advisor: Xiaolin Yang
Type of internship: Master internship, PhD internship
Duration: 6-12 months
Required educational background: Electrotechnics/Electrical Engineering
University promotor: Chris Van Hoof (KU Leuven)
Supervising scientist(s): For further information or for application, please contact Xiaolin Yang ([email protected])
The reference code for this position is 2026-INT-068. Mention this reference code in your application.
Imec allowance will be provided.
Applications should include the following information:
- resume
- motivation
- current study
Incomplete applications will not be considered.
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