Tech Briefs Magazine - June 2023 - 28

Electrical/Electronics
Solid-State Thermal Transistor
The invention is more stable than, and just as effective as, current liquid-state thermal transistors.
Hokkaido University, Sapporo, Japan
I
n modern electronics, a lot of heat is
produced as waste during usage -
hence why in-use devices become excessively
hot and require cooling
solutions. Over the last decade, the
concept of managing such heat via
electricity has been tested, leading to
the development of electrochemical
thermal transistors.
Currently, liquid-state thermal
transistors are in use, but have critical
limitations - mainly, any leakage
causes the device to stop working.
Now, a research team at Hokkaido
University has developed the first solid-state
electrochemical thermal transistor.
The invention, described in the journal
Advanced Functional Materials, is more stable
than and just as effective as current liquid-state
thermal transistors.
the switching material, " said Professor
Hiromichi Ohta. " The active material
has changeable thermal conductivity (k),
and the switching material is used to
The pioneering solid-state electrochemical thermal transistor
developed in this study. (Image: Hiromichi Ohta)
control the thermal conductivity of the
active material. "
The team constructed its thermal tran " A
thermal transistor consists broadly
of two materials: the active material and
sistor on an yttrium oxide-stabilized zirconium
oxide base - which also functioned
as the switching material - and
used strontium cobalt oxide as the active
material. Platinum electrodes were used
to supply the power necessary to control
the transistor.
The thermal conductivity of the active
material in the " on " state was comparable
to some liquid-state thermal transistors.
In general, thermal conductivity of
the active material was four times higher
in the " on " state compared to " off. "
Also, the transistor was stable over 10 use
cycles - better than some current liquid-state
thermal transistors. This behavior
was tested across more than 20 separately
fabricated thermal transistors.
The one drawback was the operating
temperature of around 300 °C.
" Our findings show that solid-state
electrochemical thermal transistors
have the potential to be just as effective
as liquid-state electrochemical thermal
transistors, with none of their limitations, "
said Ohta. " The main hurdle to developing
practical thermal transistors is the high resistance
of the switching material, and
hence a high operating temperature. This
will be the focus of our future research. "
For more information, contact Sohail
Keegan Pinto at en-press@general.
hokudai.ac.jp; +81 11-706-2185.
Implantable Chip Uses Machine Learning to Detect
Neural Disease
A neural interface can identify and suppress symptoms of various neurological disorders.
Swiss Federal Institute of Technology Lausanne, Switzerland
N
euralTree, a closed-loop neuromodulation
system-on-chip that
can detect and alleviate disease symptoms,
has been developed by researchers
Mahsa Shoaran and Stéphanie Lacour
at
Swiss
Federal
Institute
Technology
Lausanne (EPFL).
Boasting a 256-channel high-resolution
sensing array and an energy-efficient
machine learning processor,
NeuralTree can extract and classify a
broad set of biomarkers from real patient
data and animal models of disease
in-vivo, leading to a high degree
of accuracy in symptom prediction.
" NeuralTree benefits from the acof
NeuralTree
functions by extracting
neural biomarkers from brain waves.
NeuralTree functions by extracting neural biomarkers from
brain waves. (Image: Alain Herzog)
curacy of a neural network and the hardware
efficiency of a decision tree algorithm, "
said Shoaran. " It's the first time
we've been able to integrate such a complex,
yet energy-efficient neural inter28
face
for binary classification tasks, such
as seizure or tremor detection, as well as
multi-class tasks such as finger movement
classification for neuroprosthetic
applications. "
www.techbriefs.com
It then, for example, classifies the signals
and indicates whether they herald
an impending epileptic seizure or
Parkinsonian tremor. If a symptom is
detected, a neurostimulator - also
located on the chip - is activated,
sending an electrical pulse to block it.
Shoaran explained that NeuralTree's
unique design gives the system an unprecedented
degree of efficiency and
versatility compared to its contemporaries.
The system can also detect a broader
range of symptoms, which until now
have focused primarily on epileptic seizure
detection. The chip's machine learning
algorithm was trained on datasets from
both epilepsy and Parkinson's disease patients,
and accurately classified pre-recorded
neural signals from both categories.
Tech Briefs, June 2023

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Tech Briefs Magazine - June 2023

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