Researchers introduce ionic circuits that compute in water

Microprocessors in smartphones, computers and data centres process information by manipulating electrons through solid semiconductors, but the human brain has a different system that relies on the manipulation of ions in liquid to process information. Inspired by the brain, researchers have tried to develop ‘ionics’ in an aqueous solution. While ions in water move slower than electrons in semiconductors, scientists believe that the diversity of ionic species with different physical and chemical properties could be harnessed for richer and more diverse information processing. Ionic computing, however, is still in its early days, with labs having only developed individual ionic devices such as ionic diodes and transistors.

A team of researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), in collaboration with DNA Script, have developed an ionic circuit comprising hundreds of ionic transistors and performed a core process of neural net computing. Their research findings were published in Advanced Materials.

The researchers began by building a type of ionic transistor from a technique they pioneered. The transistor consists of an aqueous solution of quinone molecules, interfaced with two concentric ring electrodes with a centre disk electrode, like a bullseye. The two ring electrodes electrochemically lower and tune the local pH around the centre disk by producing and trapping hydrogen ions. A voltage applied to the centre disk causes an electrochemical reaction to generate an ionic current from the disk into the water. The reaction rate can be sped up or down — increasing or decreasing the ionic current — by tuning the local pH. The pH controls, or gates, the disk’s ionic current in the aqueous solution, creating an ionic counterpart of the electronic transistor.

The researchers then engineered the pH-gated ionic transistor so that the disk current was an arithmetic multiplication of the disk voltage and a ‘weight’ parameter representing the local pH gating the transistor. They organised these transistors into a 16 x 16 array to expand the analog arithmetic multiplication of individual transistors into an analog matrix multiplication, with the array of local pH values serving as a weight matrix encountered in neural networks. Woo-Bin Jung, a postdoctoral fellow at SEAS and the first author of the paper, said that matrix multiplication is the most prevalent calculation in neural networks for artificial intelligence. “Our ionic circuit performs the matrix multiplication in water in an analog manner that is based fully on electrochemical machinery,” Jung said.

Donhee Ham, the Gordon McKay Professor of Electrical Engineering and Applied Physics at SEAS and the senior author of the paper, said that microprocessors manipulate electrons in a digital fashion to perform matrix multiplication. “While our ionic circuit cannot be as fast or accurate as the digital microprocessors, the electrochemical matrix multiplication in water is charming in its own right and has a potential to be energy efficient,” Ham said.

The team is now looking to enrich the chemical complexity of the system. So far, the researchers have only used three to four ionic species, such as hydrogen and quinone ions, to enable the gating and ionic transport in the aqueous ionic transistor. “It will be very interesting to employ more diverse ionic species and to see how we can exploit them to make rich the contents of information to be processed,” Jung said.

Image credit: Woo-Bin Jung/Harvard SEAS