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Constructive Electronics Lab
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The lab applies technologies drawn from neural circuits to next-generation information-processing hardware.
AboutAbout the lab
Next-generation information processing, inspired by neural circuits
The Constructive Electronics Lab studies how to extract useful technologies from biological neural circuits and apply them to next-generation information-processing hardware.
The lab draws on the resilience and distributed nature that neural circuits have refined over a long course of evolution.
The Yajima Group (materials, devices, and circuits) and the Kawakami Group (circuits, architecture, and computing principles) conduct their research in collaboration.
Key terms
- Neuromorphic
- Building electronic devices and circuits guided by how neural circuits work
- Phase transition
- A phenomenon in which a substance changes from one state to another depending on conditions such as temperature. Ice turning into water is an example
- Proton
- A particle left when an electron is removed from a hydrogen atom (a hydrogen ion). It is the smallest ion
- Spike signal
- A short, pulse-like electrical signal emitted by a nerve cell
NewsLab news
A master's student received a Kyushu University Student Award
A master's student received an Outstanding Student Award from the Graduate School of Information Science and Electrical Engineering
A master's student received the Best Student Paper Award at the OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2025
ResearchResearch themes
In response to the large amounts of power and communication bandwidth consumed by accelerating digitalization, the lab aims to build energy-efficient information-processing hardware using technologies extracted from neural circuits. Drawing on circuit and materials technologies, the Yajima Group focuses on materials, devices, and circuits, and the Kawakami Group focuses on circuits, architecture, and computing principles, and the two groups work together.
*Illustrative imageEnergy-Efficient Sensors Using Phase Transitions
Using the property of phase transitions, in which physical properties change greatly across the transition, the lab develops neuromorphic digital sensors that can be embedded directly in digital circuits. By having the material itself perform the measurement, the lab aims for high-speed sensing with ultra-low power consumption.
*Illustrative imageNeuromorphic Devices Using Electrons and Protons
Focusing on the fact that protons, the smallest ions, can move freely through solids, the lab aims to control electrons and protons simultaneously inside solid-state devices and to achieve functions fundamentally different from those of conventional electronics.
*Illustrative imageEnergy-Efficient Information Processing with Spike Signals
Focusing on the fact that biological neural circuits operate connected to the real world in real time through spike signals, the lab aims for lean, real-time electronic control based on spike signals. The background is the strict low-power requirements of IoT devices.
*Illustrative imageLearning Circuits for Reservoir Computing
The lab builds ultra-low-power learning circuits based on reservoir computing algorithms, aiming to construct hardware that uses learning functions to perform anomaly detection and prediction flexibly. One example is predicting biological information on wearable devices.
MembersFaculty
The members of the Constructive Electronics Lab consist of faculty members, staff, doctoral, master's, and undergraduate students, and research students.As of 2026
- Associate ProfessorTakeaki Yajima
- Associate ProfessorSatoshi Kawakami
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: “Neuromorphic Nanowire Network” DrHughManning / CC BY-SA 4.0 (cropped)
- Energy-Efficient Sensors Using Phase Transitions: “Busch-Jaeger Elektro UP Sensor 180 Komfort 6132 - board-1129” Raimond Spekking / CC BY-SA 4.0 (cropped)
- Neuromorphic Devices Using Electrons and Protons: “Memristor ( ) (cropped)” U.S. Department of Energy from United States / Public domain (cropped)
- Energy-Efficient Information Processing with Spike Signals: “NXP PCF8577C LCD driver with I²C (Colour Corrected)” cole8888, https://flickr.com/photos/ @N05/ (aka Cole L) / CC BY-SA 2.0 (cropped)
- Learning Circuits for Reservoir Computing: “Black alder twig and water ripples at Wolfsberger Mühle” Radomianin / CC BY-SA 4.0 (cropped)
- Related lab (Electronic Materials Lab): “Propagation of light through the core of an optical fiber, Macro visualization, Demonstration of prof Angel Toshkov to students.” AZToshkov / CC BY-SA 3.0 (cropped)
- Related lab (Electron Spin Physics and Engineering Lab): “Magnetic Semiconductor ( )” National Institute of Standards and Technology / Public domain (cropped)
- Related lab (Computer Systems Architecture Lab): “Raspberry Pi 5 Hailo AI Accelerator Module” RetroEditor / CC BY 4.0 (cropped)
- Related lab (Integrated Magnetic Devices Lab): “MTJ with Waveguide Electrodes & Contacting GSG Probe for Testing” OJB Quantum / CC BY 4.0 (cropped)
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