Biologically-Inspired, Ultra-Low Power, and High-Speed Integrate-and-Fire Neuron Circuit With Stochastic Behavior Using Nanoscale Side-Contacted Field Effect Diode Technology

Enhancing power efficiency and performance in neuromorphic computing systems is critical for next-generation artificial intelligence applications. We propose the Nanoscale Side-contacted Field Effect Diode (S-FED)—a novel solution that significantly lowers power usage and improves circuit speed—enabling efficient neuron circuit design. Our proposed integrate-and-fire (IF) neuron model demonstrates remarkable performance metrics: 44 nW power consumption (85% lower than current designs), 0.964 fJ energy per spike (36% improvement over state-of-the-art), and a spiking frequency ranging from 20 to 100 MHz. Moreover, we show how to bias the circuit to enable both deterministic and stochastic operation, mimicking key computational features of biological neurons. The stochastic behavior can be precisely controlled through reference voltage modulation, achieving firing probabilities from 0% to 100% and enabling probabilistic computing capabilities. The architecture exhibits robust stability across process (channel length and doping)-voltage-temperature (PVT) variations, maintaining consistent performance with less than 7% spike amplitude variation for channel lengths from 7.5nm to 15nm, doping from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5\times 10{^{{20}}}$ </tex-math></inline-formula> cm<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${}^ - 3 $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times 10{^{{2}}} {^{{1}}}$ </tex-math></inline-formula> cm<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${}^ - 3 $ </tex-math></inline-formula>, supply voltages from 0.8V to 1.2V, and temperatures spanning −40°C to 120°C. The model features tunable thresholds (0.8V to 1.4V) and reliable operation across input spike pulse widths from 0.5 ns to 2 ns. This advancement in neuromorphic hardware paves the way for more efficient brain-inspired computing systems.