Boolean Computation in Single‐Transistor Neuron

Abstract Brain neurons exhibit far more sophisticated and powerful information‐processing capabilities than the simple integrators commonly modeled in neuromorphic computing. A biological neuron can in fact efficiently perform Boolean algebra, including linear nonseparable operations. Traditional logic circuits require more than a dozen transistors combined as NOT, AND, and OR gates to implement XOR. Lacking biological competency, artificial neural networks require multilayered solutions to exercise XOR operation. Here, it is shown that a single‐transistor neuron, harnessing the intrinsic ambipolarity of graphene and ionic filamentary dynamics, can enable in situ reconfigurable multiple Boolean operations from linear separable to linear nonseparable in an ultra‐compact design. By leveraging the spatiotemporal integration of inputs, bio‐realistic spiking‐dependent Boolean computation is fully realized, rivaling the efficiency of a human brain. Furthermore, a soft‐XOR‐based neural network via algorithm‐hardware co‐design, showcasing substantial performance improvement, is demonstrated. These results demonstrate how the artificial neuron, in the ultra‐compact form of a single transistor, may function as a powerful platform for Boolean operations. These findings are anticipated to be a starting point for implementing more sophisticated computations at the individual transistor neuron level, leading to super‐scalable neural networks for resource‐efficient brain‐inspired information processing.