This work lays out the development of a reconfigurable electronic system, which is composed of biologically relevant circuits. This system has been termed a Field-Programmable Neuron Array (FPNA) and is analogous to the more familiar Field-Programmable Gate Array (FPGA) and Field-Programmable Analog Array (FPAA). At the core of the system is an array of output somas based on previously developed bio-physically based channel models. Linking them together is a complex 2D dendrite matrix, FPAA-like floating-gate routing, and associated support circuitry. Several levels of generality give this system unprecedented re-configurability. The dendrite matrix can be arbitrarily configured so that many different topologies of dendrites can be investigated. Different soma circuits can be connected / disconnected to / from the dendrite matrix. Outputs from the somas can be arbitrarily routed to input synapses that exist at each dendrite node as well as the soma nodes. Lastly, the dynamics of each node consist of a mixture of individually tunable parts and global biases. All of this can be configured in concert to investigate neural circuits that exist in biological systems. This chip will have a significant impact on research in many fields including neuroscience, neuromorphic engineering, and robotics. This chip will allow for rapid prototyping of spinal circuits. Since the fundamental circuits of the system are chosen to be biologically relevant, outputs from the various nodes should also be relevant, thus yielding itself to use by neuroscientists. This system also provides a tool by where biological systems can be emulated in real-world electronic systems. Solutions to many problems faced by roboticists (such as bi-pedal standing / walking / running / jumping / climbing and the transitions between states) are present in biology. By providing a chip that can duplicate the same neural circuits that are responsible for these processes in the biology, the hypothesis is that researchers can begin to solve some of the same types of problems in artificial systems.

Inspired by neurobiological systems, Spiking Neural Networks (SNNs) are gaining an increasing interest in the field of bio-inspired machine learning. Neurons, as central processing and short-term memory units of biological neural systems, are thus at the forefront of cutting-edge research approaches. The realization of CMOS circuits replicating neuronal features, namely the integration of action potentials and firing according to the all-or-nothing law, imposes various challenges like large area and power consumption. The non-volatile storage of polarization states and accumulative switching behavior of nanoscale HfO₂ - based Ferroelectric Field-Effect Transistors (FeFETs), promise to circumvent these issues. In this paper, we propose two FeFET-based neuronal circuits emulating the Integrate-and-Fire (I&F) behavior of biological neurons on the basis of SPICE simulations. Additionally, modulating the depolarization of the FeFETs enables the replication of a biology-based concept known as membrane leakage. The presented capacitor-free implementation is crucial for the development of neuromorphic systems that allow more complex features at a given area and power constraint.

Neuron is the basic computing unit in brain-inspired neural networks. Although a multitude of excellent artificial neurons realized with conventional transistors have been proposed, they might not be energy and area efficient in large-scale networks. The recent discovery of ferroelectricity in hafnium oxide (HfO₂) and the related switching phenomena at the nanoscale might provide a solution. This study employs the newly reported accumulative polarization reversal in nanoscale HfO₂-based ferroelectric field-effect transistors (FeFETs) to implement two key neuronal dynamics: the integration of action potentials and the subsequent firing according to the biologically plausible all-or-nothing law. We show that by carefully shaping electrical excitations based on the particular nucleation-limited switching kinetics of the ferroelectric layer further neuronal behaviors can be emulated, such as firing activity tuning, arbitrary refractory period and the leaky effect. Finally, we discuss the advantages of an FeFET-based neuron, highlighting its transferability to advanced scaling technologies and the beneficial impact it may have in reducing the complexity of neuromorphic circuits.