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Neuromorphic devices
Ferroelectric and antiferroelectric transistors for compact neurons, adaptive dynamics, low-energy switching, and CMOS-compatible integration.
Brain-inspired devices · circuits · AI
Engineering efficient intelligence from sensors to systems.
We develop neuromorphic devices, circuits, and sensor systems that process information more like the nervous system: event-driven, adaptive, compact, and energy-efficient.
fJ
Target energy scale for compact spiking neurons
SNN
Event-driven learning and inference at the edge
AI × HW
Co-design across materials, devices, circuits, and algorithms
Adaptive spiking response
Research themes
Hardware-native intelligence for physical-world AI.
Our research connects emerging nanoelectronic devices with neuromorphic circuits, event-driven sensing, and spiking neural networks.
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Brain-like circuits
Circuit-device co-design of spiking neurons and synaptic interfaces that support leakage, integration, reset, adaptation, and dynamic thresholds.
✋
Neuromorphic sensing
Tactile and wearable sensing systems that transform rich physical signals into sparse, event-driven representations for robotics and health.
∞
Spiking neural networks
Efficient SNN algorithms and benchmarks for classification, perception, sensor fusion, and edge deployment.
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Digital biomarkers
Wearable and AI-assisted measurements for gait, rehabilitation, early disease detection, and longitudinal monitoring.
♻
Sustainable intelligence
Low-carbon materials, self-powered sensors, and event-driven edge AI for sustainable manufacturing and energy-aware computing.
Our approach
Co-design from material physics to system behavior.
We do not treat devices, circuits, and algorithms as isolated layers. Instead, we use cross-layer co-design: physical device dynamics become useful computational primitives; circuits exploit those dynamics; algorithms are adapted to the hardware; and system-level benchmarks quantify efficiency, latency, and robustness.
01
Materials and device dynamics
Tune hysteresis, steep-slope behavior, volatility, leakage, relaxation, endurance, and variability.
02
Compact neuromorphic circuits
Map device physics into spiking neuron functions with minimal area and energy overhead.
03
Event-driven sensing
Encode touch, motion, gait, and manufacturing signals as sparse temporal events.
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System validation
Benchmark SNN accuracy, energy per inference, latency, robustness, and deployability.
Selected projects
Building blocks for embodied and edge AI.
Edit the project names, funders, and dates directly in this file as your group evolves.
Flagship direction
Brain-like artificial neurons with circuit-device co-design
Developing compact antiferroelectric/ferroelectric transistor-based neuron circuits that combine efficiency, rich neuronal dynamics, and scalability for high-efficiency SNNs.
Neuromorphic tactile systems
Artificial tactile perception for robotic and prosthetic systems using sensors, spiking encoders, and hardware-aware learning.
AI-driven digital biomarkers
Wearable sensing and machine learning for gait analysis, rehabilitation monitoring, and early detection of abnormal trajectories.
Low-carbon sensing–intelligence–control
Cellulose/lignin-based self-powered sensors coupled to edge AI for sustainable process monitoring and optimization.
Publications
Selected outputs.
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Science · 2024
Neuromorphic tactile system for artificial touch perception
Selected publication · tactile sensing · neuromorphic processing · embodied AI.
Nano Energy
Artificial tactile system with receptive-field-inspired sensing
Selected publication · sensor arrays · artificial neurons · spatial resolution.
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Publication title, authors, journal, year
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News
Latest from the lab.
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2026
New research group homepage launched
A clean starting point for sharing research themes, publications, open positions, and collaboration opportunities.
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New paper, grant, award, or talk
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Team
People.
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Libo Chen
Assistant Professor · Department of Electrical Engineering · Uppsala University
PhD students and postdocs
We welcome motivated candidates in neuromorphic devices, CMOS circuits, SNNs, wearable sensing, and edge AI.
Master thesis projects
Possible topics include spiking neuron modeling, tactile data processing, digital biomarkers, and neuromorphic sensor systems.
Collaborators
We collaborate across electrical engineering, materials science, robotics, AI, clinical research, and sustainable manufacturing.
Contact
Interested in collaboration?
We are open to academic and industrial collaborations in neuromorphic hardware, embodied AI, wearable sensing, digital biomarkers, and sustainable edge intelligence.