Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks. To achieve a large scale network akin to biology, a power and area efficient electronic neuron is essential. Earlier, we had demonstrated an LIF neuron by a novel 4-terminal impact ionization based n+/p/n+ with an extended gate (gated-INPN) device by physics simulation. Excellent improvement in area and power compared to conventional analog circuit implementations was observed. In this paper, we propose and experimentally demonstrate a compact conventional 3-terminal partially depleted (PD) SOI- MOSFET (100 nm gate length) to replace the 4-terminal gated-INPN device. Impact ionization (II) induced floating body effect in SOI-MOSFET is used to capture LIF neuron behavior to demonstrate spiking frequency dependence on input. MHz operation enables attractive hardware acceleration compared to biology. Overall, conventional PD-SOI-CMOS technology enables very-large-scale-integration (VLSI) which is essential for biology scale (~1011 neuron based) large neural networks.
The potential impact of high permittivity gate dielectrics on device short channel and circuit performance is studied over a wide range of dielectric permittivities (gate) using two-dimensional (2-D) device and Monte Carlo simulations. The gate-to-channel capacitance and parasitic fringe capacitances are extracted using a highly accurate three-dimensional (3-D) capacitance extractor. It is observed that there is a decrease in parasitic outer fringe capacitance and gate-to-channel capacitance in addition to an increase in internal fringe capacitance, when the conventional silicon dioxide is replaced by a high-gate dielectric. The lower parasitic outer fringe capacitance is beneficial for the circuit performance, while the increase in internal fringe capacitance and the decrease in the gate-to-channel capacitance will degrade the short channel performance contributing to higher DIBL, drain leakage, and lower noise margin. It is shown that using lowgate sidewalls with high-gate insulators can decrease the fringing-induced barrier lowering. Also, from the circuit point of view, for the 70-nm technology generation, the presence of an optimum gate for different target subthreshold leakage currents has been identified.
The human brain comprises about a hundred billion neurons connected through quadrillion synapses. Spiking Neural Networks (SNNs) take inspiration from the brain to model complex cognitive and learning tasks. Neuromorphic engineering implements SNNs in hardware, aspiring to mimic the brain at scale (i.e., 100 billion neurons) with biological area and energy efficiency. The design of ultra-energy efficient and compact neurons is essential for the large-scale implementation of SNNs in hardware. In this work, we have experimentally demonstrated a Partially Depleted (PD) Silicon-On-Insulator (SOI) MOSFET based Leaky-Integrate & Fire (LIF) neuron where energy-and area-efficiency is enabled by two elements of design -first tunneling based operation and second compact sub-threshold SOI control circuit design. Band-to-Band Tunneling (BTBT) induced hole storage in the body is used for the "Integrate" function of the neuron. A compact control circuit "Fires" a spike when the body potential exceeds the firing threshold. The neuron then "Resets" by removing the stored holes from the body contact of the device. Additionally, the control circuit provides "Leakiness" in the neuron which is an essential property of biological neurons. The proposed neuron provides 10× higher area efficiency compared to CMOS design with equivalent energy/spike. Alternatively, it has 10 4 × higher energy efficiency at area-equivalent neuron technologies. Biologically comparable energy-and areaefficiency along with CMOS compatibility make the proposed device attractive for large-scale hardware implementation of SNNs.
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