Patil, ShubhamShubhamPatilSakhuja, JayatikaJayatikaSakhujaBiswas, AnmolAnmolBiswasHajare, HemantHemantHajareKadam, AbhishekAbhishekKadamDeshmukh, ShreyasShreyasDeshmukhSingh, Ajay KumarAjay KumarSinghLashkare, SandipSandipLashkareMohapatra, Nihar RanjanNihar RanjanMohapatraGanguly, UdayanUdayanGanguly2025-08-312025-08-312025-01-01[9798331504168]10.1109/EDTM61175.2025.110406802-s2.0-105010824644https://d8.irins.org/handle/IITG2025/28344In this work, we show the electrical control in the ultra-energy and area-efficient BTBT-based Si-neuron and the impact on network performance. We show the control of gate bias and current threshold on the spiking threshold and frequency. Finally, we show the impact of such design space on SNN performance and a 10-layer spiking Convolutional Neural Network (CNN). The result demonstrates that neurons' post-fabrication electrical tuning capability is essential for SNN performance improvement.falseConference Paper20250cpConference Proceeding0