While CMOS technology is currently reaching its limits in power consumption and circuit density, a challenger is emerging from the analogy between biology and silicon. Hardware-based neural networks may drive a new generation of bio-inspired computers by the urge of a hardware solution for real-time applications. This paper redesigns a previous proposed electronic neuron (e-Neuron) in a higher firing rate to reduce the silicon area and highlight a better energy efficiency trade-off. Besides, an innovative schematic is proposed to state an e-Neuron library based on Izhikevichs model of neural firing patterns. Both e-Neuron circuits are designed using 55 nm technology node. Physical design of transistors in weak inversion are discussed to a minimal leakage. Neural firing pattern behaviors are validated by post-layout simulations, demonstrating the spike frequency adaptation and the rebound spikes due to postinhibitory effect in LTS e-Neuron. Presented results suggest that the time to rebound spikes is dependent of the excitation current amplitude. Both e-Neurons have presented a fF/spike energy efficiency and a smaller silicon area in comparison to Izhikevichs library propositions in the literature. CCS CONCEPTS• Hardware → Analog and mixed-signal circuit synthesis; Standard cell libraries; Emerging architectures.

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