  Brain-inspired computation promises complex cognitive tasks at biological
energy efficiencies. The brain contains $10^4$ synapses per neuron. Hence,
ultra-low energy, high-density synapses are needed for spiking neural networks
(SNN). In this paper, we use tunneling enabled CTF (Charge Trap Flash) stack
for ultra-low-energy operation (1F); Further, CTF on an SOI platform and
back-to-back connected pn diode and Zener diode (2D) prevent parasitic leakage
to preserve energy advantage in array operation. A bulk $100 {\mu}m $ x $100
{\mu}m$ CTF operation offers tunable, gradual conductance change $({\Delta}G)
i.e. 10^4 $levels, which gives $100$x improvement over literature. SPICE
simulations of 1F2D synapse shows ultra-low energy $(\leqslant 3 fJ/pulse)$ at
180 nm node for long-term potentiation (LTP) and depression (LTD), at 180nm
node for long-term potentiation (LTP) and depression (LTD), which is comparable
to energy estimate in biological synapses (10 fJ). A record low learning rate
(i.e., maximum ${\Delta}G< 1%$ of G-range) is observed - which is tunable.
Excellent reliability ($>10^6 endurance cycles at full conductance swing) is
observed. Such a highly energy efficient synapse with tunable learning rate on
the CMOS platform is a key enabler for the human-brain-scale systems. Keywords:
Spiking Neural Network; Charge trap flash, SONAS, Fowler-Nordheim Tunneling,
Synapse
