In analog neuromorphic chips, designers can embed computing primitives in the intrinsic physical properties of devices and circuits, heavily reducing device count and energy consumption, and enabling high parallelism, because all devices are computing simultaneously. Neural network parameters can be stored in local analog non-volatile memories (NVMs), saving the energy required to move data between memory and logic. However, the main drawback of analog sub-threshold electronic circuits is their dramatic temperature sensitivity. In this paper, we demonstrate that a temperature compensation mechanism can be devised to solve this problem. We have designed and fabricated a chip implementing a two-layer analog neural network trained to classify low-resolution images of handwritten digits with a low-cost single-poly complementary metal-oxide-semiconductor (CMOS) process, using unconventional analog NVMs for weight storage. We demonstrate a temperature-resilient analog neuromorphic chip for image recognition operating between 10°C and 60°C without loss of classification accuracy, within 2% of the corresponding software-based neural network in the whole temperature range.

This paper presents the experimental assessment of a recently proposed ultra-low voltage fully-differential inverter-based amplifier. Thanks to the novel common-mode stabilization loop, the amplifier reaches effective differential operations without compromising the output differential performance even at supply voltages as low as 150 mV. Experimental measurements demonstrate the amplifier functionality at supply voltages between 0.15 V and 0.5 V. In particular, a differential dc voltage gain of 22 dB, a gain-bandwidth product of 750 Hz with a capacitive load of 100 pF, and a static current consumption of only 57 nA are measured at Vdd= 0.3 V, with an area consumption of only 800 μm2.

Some applications require electronic systems to operate at extremely high temperature. Extending the operating temperature range of automotive-grade CMOS processes — through the use of dedicated design techniques — can provide an important cost-effective advantage. We present a second-order discrete-time delta-sigma analog-to-digital converter operating at a temperature of up to$250~^{\circ }$C, well beyond the$175~^{\circ }$C qualification temperature of the automotive-grade CMOS process used for its fabrication (XFAB XT018). The analog-to-digital converter incorporates design techniques that are effective in mitigating the adverse effects of the high temperature, such as increased leakage currents and electromigration. We use configurations of dummy transistors for leakage compensation, clock-boosting methods to limit pass-gate cross-talk, and we optimized the circuit architecture to ensure stability and accuracy at high temperature. Comprehensive measurements demonstrate that the analog-to-digital converter achieves a signal-to-noise ratio exceeding 93 dB at$250~^{\circ }$C over a signal bandwidth of 146 Hz, with an effective number of bits of 12, and a power consumption of only 44 mW. The die area of the converter is only 0.065 mm2and the area overhead of the high-temperature mitigation circuits is only 13.7%. The Schreier Figure of Merit is 140 dB at the maximum temperature of$250~^{\circ }$C, proving the potential of the circuit for reliable operation in challenging applications such as gas and oil extraction and aeronautics.

The aim of the paper is to describe a project concerned with the development of a daily monitoring system for elderly people living alone. The system relies on a new non invasive type of communication based on devices commonly owned by elderly people, to reduce initial cost of deployment. All collected data could then be analyzed by a Medical Doctor to monitor the real current situation of the patient using open source instrument to generate analysis, report and data mining tasks.