In advanced high-speed integrated systems, the widely distributed and proliferation of temperature sensors to detect hotspots improve the robustness and reliability of the system by preventing overheating. Low area and low energy consumption are essential for integrated temperature sensors in such applications. The band-to-band tunneling (BTBT) current in PDSOI technology increases linearly with temperature. We demonstrate a BTBT-based quasi-linear temperature-dependent oscillator, thermillator. The fabricated thermillator has a low footprint (42.3 μm², 10× less than state-of-the-art temperature sensing cores) and enables low energy consuming temperature to digital converter (0.33 nJ energy/conversion) in GF45RFSOI technology. The proposed thermillator facilitates a low power and low footprint temperature sensor (0 oC to 85 oC) with a simple counter-based digital readout with a best-in-class resolution FoM of 0.16 pJK².

Efficient and compact neurons with low power consumption are crucial when designing large-scale spiking neural networks (SNNs) for hardware implementation. Many architectures in the literature showcase different spike patterns associated with biological neurons. However, using bulky capacitors to generate the different time constants related to complex neuron patterns makes these circuits area inefficient. This paper presents a band-to-band-tunneling (BTBT) based energy-efficient and compact neuron capable of producing various spike patterns. The BTBT region's extremely low current enables different time constants while eliminating the need of bulky capacitors. The circuit is based on the Izhikevich neuron model. The proposed circuit is designed in Silicon on Insulator technology to exhibit important firing patterns observed in the biological cortex, viz. regular spiking, fast-spiking, and chattering, and it is fine-tuned for efficient operation at low subthreshold voltages. This circuit utilizes only 129 μm 2 area and consumes only 6.7 fJ energy per spike (approximately 40% lower area and energy per spike than state-of-the-art multi-mode neurons) in G45RFSOI technology.