This work presents a single-stage, inverter-based, pseudo-differential amplifier that can work with ultra-low supply voltages. A novel common-mode stabilization loop allows proper differential operations, without impacting over the output differential performance. Electrical simulations show the effectiveness of this amplifier for supply voltages in the range of 0.3–0.5 V. In particular, a dc voltage gain of 25.16 dB, a gain-bandwidth product of 131.9 kHz with a capacitive load of 10 pF, and a static current consumption of only 557 nA are estimated at V_DD = 0.5 V. Moreover, the circuit behavior with respect to process and temperature variations was verified. Finally, the proposed amplifier is employed in a switched-capacitor integrator and in a sample-and-hold circuit to prove its functionality in case-study applications.

This article presents a two-times interleaved, loop-unrolled SAR analog-to-digital converter (ADC) operational from 300 down to 4.2 K. The 6–8-bit resolution and the sampling speed up to 1 GS/s are targeted at digitizing the multi-channel frequency-multiplexed input in a spin-qubit reflectometry readout for quantum computing. To optimize the circuit for the altered device behavior at cryogenic temperatures, a modified common-mode switching scheme is adopted as well as a flexible calibration. The design is implemented in 40-nm CMOS technology and achieves 36.2-dB signal to noise and distortion ratio (SNDR) for Nyquist input at 4.2 K while maintaining a Walden figure of merit (FOMW) of 200 pJ/conv-step (for a 10.8-mW power consumption), including the clock receiver, and 15 pJ/conv-step (for a 0.8-mW power consumption) for just the core ADC. With these specifications, the ADC can support the simultaneous readout of 20 qubit channels with a power consumption of 0.5 mW/qubit, thus advancing toward the full integration of the cryogenic readout for future large-scale quantum processors.

In the modern era of artificial intelligence, increasingly sophisticated artificial neural networks (ANNs) are implemented, which pose challenges in terms of execution speed and power consumption. To tackle this problem, recent research on reduced-precision ANNs opened the possibility to exploit analog hardware for neuromorphic acceleration. In this scenario, photonic-electronic engines are emerging as a short-medium term solution to exploit the high speed and inherent parallelism of optics for linear computations needed in ANN, while resorting to electronic circuitry for signal conditioning and memory storage. In this paper we introduce a precision-scalable integrated Photonic-Electronic Multiply-Accumulate Neuron (PEMAN). The proposed device relies on (i) an analog photonic engine to perform reduced-precision multiplications at high speed and low power, and (ii) an electronic front-end for accumulation and application of the nonlinear activation function by means of a nonlinear encoding in the analog-to-digital converter (ADC). The device has been numerically validated through cosimulations to perform multiply-accumulate operations (MAC). Simulations are based on the iSiPP50G SOI process for the photonic engine and a commercial 28 nm CMOS process for the electronic front-end. The PEMAN exhibits a multiplication accuracy of 6.1 ENOB up to 10 GMAC/s, while it can perform computations up to 56 GMAC/s with a reduced accuracy down to 2.1 ENOB. The device can trade off speed and power consumption with resolution, significantly outperforming its analog electronics counterparts both in terms of speed and energy consumption. With respect to other photonic ANNs, the PEMAN has comparable speed and energy consumption with a higher resolution, while outperforming them by a hundredfold in the fan-in, which opens the possibility to accelerate more complex networks.

In this paper, we discuss the challenges at the device and circuit level that must be addressed to design reliable silicon CMOS integrated circuits operating in high-temperature environments. We present experimental results on representative devices fabricated with a 180 nm CMOS SOI platform, which have been characterized up to 300°C, discuss issues arising at high temperature, and propose possible solutions. A BJT-based temperature sensor core is also described and evaluated across the same extended temperature range. We also propose and discuss design criteria optimized for a wide range of operating temperature. A sufficient on/off current ratio can be achieved by taking advantage of the isolation provided by low-leakage CMOS SOI process, while operation at low current density must be ensured to mitigate electromigration effects. Within these conditions, low-power precise sensing circuits can be effectively implemented with operating temperature up to 300°C, that are extremely relevant for industrial, mobility, and space applications.

A voltage reference is strictly required for sensor interfaces that need to perform nonratiometric data acquisition. In this work, a voltage reference capable of working with supply voltages down to 0.5 V is presented. The voltage reference was based on a classic CMOS bandgap core, properly modified to be compatible with low-threshold or zero-threshold MOSFETs. The advantages of the proposed circuit are illustrated with theoretical analysis and supported by numerical simulations. The core was combined with a recently proposed switched capacitor, inverter-like integrator implementing offset cancellation and low-frequency noise reduction techniques. Experimental results performed on a prototype designed and fabricated using a commercial 0.18 $\mathsf{\mu }$m CMOS process are presented. The prototype produces a reference voltage of 220 mV with a temperature sensitivity of 45 ppm/°C across a 10–50 °C temperature range. The proposed voltage reference can be used to source currents up to 100 $\mathsf{\mu }$A with a quiescent current consumption of only 630 nA.