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Integrated Nanosystems Lab
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The lab builds brain-inspired computing systems from nanomaterials and devices.
AboutAbout the lab
From nanomaterials, embedding intelligence in circuits
The Integrated Nanosystems Lab takes on the challenge of merging circuit engineering and device engineering to build integrated nanosystems that make the most of both.
Its research spans semiconductor physics, circuit theory, information science, nonlinear theory, and neuroscience.
After understanding the properties of new nanomaterials and devices, the lab designs circuits and information processing systems matched to their characteristics.
Together with the Integrated Architecture Lab, it forms the Advanced LSI Engineering research group (LALSIE), promoting industry-academia and inter-university collaboration.
Key terms
- Reservoir computing
- A method that uses a complex, dynamically fluctuating system directly for computation and trains only the weights at the output
- Neuromorphic
- An approach that imitates the workings of neurons in the brain and performs computation with circuits and devices
- Memristive device
- A device (memristor) whose resistance changes depending on the history of the current that has flowed through it, allowing memory and computation to take place in the same place
NewsLab news
A doctoral student received the Student Paper Award at NSCP'26
A paper on a CMOS reservoir chip has been accepted for publication in npj Unconventional Computing
Received the Best Paper Award at MITA/CCMS 2025
A student received the 2024 Encouragement Award from the IEICE Technical Committee on Complex Communication Sciences (CCS)
A student received the Student Presentation Award at the International Symposium on Neuromorphic AI Hardware
ResearchResearch themes
The lab aims to go beyond the end of Moore's Law and open up computing based on new principles. From nanomaterials such as carbon nanotubes and polymer networks to analog circuits, it studies next-generation information processing hardware that learns from the brain.
*Illustrative imageReservoir Computing
The lab processes time-series signals using analog CMOS circuits, FPGAs, and physical materials such as carbon nanotubes and polymers. It developed a subthreshold analog reservoir chip, which was published in npj Unconventional Computing (2026).
*Illustrative imageNeuromorphic Computing
The lab builds brain-inspired hardware using three-dimensional conductive polymer networks, magnetic tunnel junctions, and atomic switches. It is also designing and evaluating predictive coding circuits based on the free energy principle.
*Illustrative imageNanodevice-Integrated Circuits
The lab co-designs memristive devices made from nanomaterials such as carbon nanotubes, graphene, and sumanene together with CMOS circuits. It also works on developing new switching elements that exploit electrochemical reactions and atomic switches.
*Illustrative imageStochastic and Nonlinear Computing
The lab handles signal processing that uses stochastic resonance and noise, reaction-diffusion devices, and low-power computation based on stochastic computing. It implements spike-timing-dependent plasticity (STDP) in circuits, aiming for analog LSI capable of learning.
MembersFaculty
The members of the Integrated Nanosystems Lab are faculty members, technical staff, academic researchers, administrative assistants, and doctoral, master's, and undergraduate students.As of 2026
- ProfessorTetsuya Asai
- Associate ProfessorKota Ando
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Нейрон коры с множеством дендритных шипиков” Sergb95 / CC BY-SA 4.0 (cropped)
- Reservoir Computing: “Triangular wave and square wave on oscilloscope screen (cropped)” User:Xato / CC0 (cropped)
- Neuromorphic Computing: “Cerebellum PurkinjeCells” Sbrandner / Public domain (cropped)
- Nanodevice-Integrated Circuits: “Carbon nanotube chiral povray” Arnero / Public domain (cropped)
- Stochastic and Nonlinear Computing: “Patterns in the Belousov–Zhabotinsky reaction” Philip Ball - courtesy of Michael C. Rogers and Stephen Morris, University of Toronto / CC BY 4.0 (cropped)
- Related lab (Functional Communication and Sensing Lab): “Pier2.3 OV7120 CMOS image sensor closeup” Phiarc / CC BY-SA 4.0 (cropped)
- Related lab (Coherent Light Lab): “SACLA Undulator” SRIA / CC BY-SA 4.0 (cropped)
- Related lab (Integrated Architecture Lab): “A semiconductor wafer being removed from processing equipment” YouTube — Purdue Engineering / CC BY 4.0 (cropped)
- Related lab (Nanoelectronic Devices Lab): “CMOS magnetische Blasendomänen” Matesy GmbH / CC BY-SA 3.0 (cropped)
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