Investigating the Impact of BTI and HCI on Log-Domain Based Mihalas–Niebur Neuron Circuit Shaik Jani Babu 1(&) , Anish Vipperla 1 , Haarica Vinayaga Murthy 1 , Chintakindi Sandhya 1 , Siona Menezes Picardo 2 , Sonal Singhal 1 , and Nilesh Goel 2 1 Shiv Nadar University, Greater Noida, India skjanibabu786@gmail.com 2 Birla Institute of Technology and Science Pilani, Dubai Campus, Dubai, UAE goel.nilesh@gmail.com Abstract. Neuromorphic circuits are becoming quite popular due to their ability to mimic the structure and behavior of human brain. Current research focuses on approximating spiking biological neuron behavior. Various neuron models have been proposed in the past that aid in investigating the behavior of neuronal systems mathematically. Mihalas–Niebur (MN) neuron model is one among them. In this paper log-domain based MN neuron model is implemented at 45 nm technology node. The paper studies the effects of process-temperature variations and also investigates the impact of Hot Carrier Injection (HCI), Bias Temperature Instability (BTI) on the performance of MN circuit. Average power consumption and spiking frequency are chosen as key performance measures to analyze the circuit performance before and after degradation. Keywords: Neuromorphic circuits Á Mihalas-Niebur neuron Á Process corner Á Bias temperature instability Á Hot carrier injection 1 Introduction Neuromorphic computing has been referred to a variety of brain-inspired computers, devices, and models that differentiate the predominant Von Neumann architecture. Neuromorphic engineering aims to emulate human cognition enabling architectures to deal with problems such as uncertainty and ambiguity. Mimicking the brain would give them the ability to adapt and learn from unstructured stimuli with the energy ef ficiency of the human brain [1]. Neuromorphic chips have potential to accomplish tasks such as image and pattern recognition quite ef ficiently which is still challenging for modern Von Neumann based hardware architectures. Bulk of the current research in neuromorphic circuitry is aimed at approximating spiking biological neuron behavior. Mathematical descriptions of neural dynamics have been introduced by Hodgkin and Huxley model. Although it is capable of describing several types of neuron behaviors, it suffers from the requirement of a large number of parameters resulting in increased circuit complexity. In order to overcome this obstacle, the generalized Leaky Integrate-and-Fire (LIF) model is © Springer Nature Singapore Pte Ltd. 2020 N. Goel et al. (eds.), Modelling, Simulation and Intelligent Computing, Lecture Notes in Electrical Engineering 659, https://doi.org/10.1007/978-981-15-4775-1_57