Artificial synaptic semiconductor device latest in next-generation brain-mimicking computing

Neuromorphic computing system technology mimicking the human brain has emerged and overcome the limitation of excessive power consumption regarding the existing von Neumann computing method. A high-performance, analog artificial synapse device, capable of expressing various synapse connection strengths, is required to implement a semiconductor device that uses a brain information transmission method. This method uses signals transmitted between neurons when a neuron generates a spike signal.

However, considering conventional resistance-variable memory devices widely used as artificial synapses, as the filament grows with varying resistance, the electric field increases, causing a feedback phenomenon, resulting in rapid filament growth. Therefore, it is challenging to implement considerable plasticity while maintaining analog (gradual) resistance variation concerning the filament type.

The Korea Institute of Science and Technology (KIST, President Yoon Seok-jin), led by Dr. YeonJoo Jeong's team at the Center for Neuromorphic Engineering, solved the limitations of analog synaptic characteristics, plasticity and information preservation, which are chronic obstacles regarding memristors, neuromorphic semiconductor devices. He announced the development of an artificial synaptic semiconductor device capable of highly reliable neuromorphic computing.

The KIST research team fine-tuned the redox properties of active electrode ions to solve small synaptic plasticity hindering the performance of existing neuromorphic semiconductor devices. Furthermore, various transition metals were doped and used in the synaptic device, controlling the reduction probability of active electrode ions. It was discovered that the high reduction probability of ions is a critical variable in the development of high-performance artificial synaptic devices.

Therefore, a titanium transition metal, having a high ion reduction probability, was introduced by the research team into an existing artificial synaptic device. This maintains the synapse's analog characteristics and the device plasticity at the synapse of the biological brain, approximately five times the difference between high and low resistances. Furthermore, they developed a high-performance neuromorphic semiconductor that is approximately 50 times more efficient.

Additionally, due to the high alloy formation reaction concerning the doped titanium transition metal, the information retention increased up to 63 times compared with the existing artificial synaptic device. Furthermore, brain functions, including long-term potentiation and long-term depression, could be more precisely simulated.

The team implemented an artificial neural network learning pattern using the developed artificial synaptic device and attempted artificial intelligence image recognition learning. As a result, the error rate was reduced by more than 60% compared with the existing artificial synaptic device; additionally, the handwriting image pattern (MNIST) recognition accuracy increased by more than 69%. The research team confirmed the feasibility of a high-performance neuromorphic computing system through this improved the artificial synaptic device.

Dr. Jeong of KIST stated, "This study drastically improved the synaptic range of motion and information preservation, which were the greatest technical barriers of existing synaptic mimics."

"In the developed artificial synapse device, the device's analog operation area to express the synapse's various connection strengths has been maximized, so the performance of brain simulation-based artificial intelligence computing will be improved."

Additionally, he mentioned, "In the follow-up research, we will manufacture a neuromorphic semiconductor chip based on the developed artificial synapse device to realize a high-performance artificial intelligence system, thereby further enhancing competitiveness in the domestic system and artificial intelligence semiconductor field."

Research Report:Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing

Related Links
National Research Council of Science and Technology
Computer Chip Architecture, Technology and Manufacture
Nano Technology News From SpaceMart.com

Tweet

Thanks for being there;
We need your help. The SpaceDaily news network continues to grow but revenues have never been harder to maintain.

With the rise of Ad Blockers, and Facebook - our traditional revenue sources via quality network advertising continues to decline. And unlike so many other news sites, we don't have a paywall - with those annoying usernames and passwords.

Our news coverage takes time and effort to publish 365 days a year.

If you find our news sites informative and useful then please consider becoming a regular supporter or for now make a one off contribution.

SpaceDaily Monthly Supporter
$5+ Billed Monthly

paypal only

SpaceDaily Contributor
$5 Billed Once

credit card or paypal

Foxconn strikes $19.4 bn deal to make chips in India
Mumbai (AFP) Sept 14, 2022
Taiwanese electronics giant Foxconn will invest $19.4 billion to make semiconductors in India with local conglomerate Vedanta, backed by New Delhi's push to boost tech self-reliance after a global chip shortage. Semiconductors are an essential component of nearly all modern electronics, from smartphones to kitchen appliances and cars, but the coronavirus pandemic kneecapped global production and leading manufacturers are still struggling to meet demand. India approved a $10 billion incentive pla ... read more