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This article presents a readout scheme for analog in-memory computing (AIMC) based on an embedded phase-change memory (ePCM). Conductance time drift is overcome with a hardware compensation technique based on a reference cell conductance tracking (RCCT). Accuracy drop due to circuits mismatch and variability involved in the computational chain are minimized with an optimized iterative program-and-verify algorithm applied to the phase-change memory (PCM) devices. The proposed AIMC scheme is designed and manufactured in a 90-nm STMicroelectronics CMOS technology, with the aim of adding a signed multiply-and-accumulate (MAC) computation feature to a Ge-Rich GeSbTe (GST) embedded PCM array. Experimental characterizations are performed under different operating conditions and show that the mean MAC decrease in time is approximately null at room temperature and reduced by a factor of 3 after 64-h bake at $85~{^{\circ }}$ C. Based on several MAC operations, the estimated $512\times 512$ matrix-vector-multiplication (MVM) accuracy is 97.4%, whose decrease in time is less than 3% in the worst case.

This paper presents a passive Envelope Detector (ED) to be used for reception of OOK-modulated signals, such as in Wake-Up Receivers employed within Wireless Sensor Networks, widely used in the IoT. The main goal is implementing a temperature compensation mechanism in order to keep the passive ED input resistance roughly constant over temperature, making it a constant load for the preceding matching network and ultimately keeping the overall receiving chain sensitivity constant over temperature. The proposed ED was designed using STMicroelectronics 90 nm CMOS technology to receive 1 kbps OOK-modulated packets with a 433 MHz carrier frequency and a 0.6 V supply. The use of a block featuring a Proportional-to-Absolute Temperature (PTAT) current yields a 5 dB reduction in sensitivity temperature variation across the −40 °C to 120 °C range. Moreover, two different implementations were compared, one targeting minimal mismatch and the other one targeting minimal area. The minimal area version appears to be better in terms of estimated overall chain sensitivity at all temperatures despite a higher sensitivity spread.

The Internet of Things (IoTs) is growing fast both in terms of number of devices connected and of complexity of deployments and applications. Several research studies an- alyzing the economical impact of the IoT worldwide identify the interoperability as one of the main boosting factor for its growth, thanks to the possibility to unlock novel commercial opportunities derived from the integration of heterogeneous systems which are currently not interconnected. However, at present, interoperability constitutes a relevant practical issue on any IoT deployments that is composed of sensor platforms mapped on different wireless technologies, network protocols or data formats. The paper addresses such issue, and investigates how to achieve effective data interoperability and data reuse on complex IoT deployments, where multiple users/applications need to consume sensor data produced by heterogeneous sensor networks. We propose a generic three-tier IoT architecture, which decouples the sensor data producers from the sensor data consumers, thanks to the intermediation of a semantic broker which is in charge of translating the sensor data into a shared ontology, and of providing publish-subscribe facilities to the producers/consumers. Then, we describe the real-world implementation of such architecture devised at the Advanced Research Center on Electronic System (ARCES) of the University of Bologna. The actual system collects the data produced by three different sensor networks, integrates them through a SPARQL Event Processing Architecture (SEPA), and supports two front- end applications for the data access, i.e. a web dashboard and an Amazon Alexa voice service.

We present a self-sustained battery-less multi-sensor platform with RF harvesting capability down to −17 dBm and implementing a standard DASH7 wireless communication interface. The node operates at distances up to 17 m from a 2 W UHF carrier. RF power transfer allows operation when common energy scavenging sources (e.g., sun, heat, etc.) are not available, while the DASH7 communication protocol makes it fully compatible with a standard IoT infrastructure. An optimized energy-harvesting module has been designed, including a rectifying antenna (rectenna) and an integrated nano-power DC/DC converter performing maximum-power-point-tracking (MPPT). A nonlinear/electromagnetic co-design procedure is adopted to design the rectenna, which is optimized to operate at ultra-low power levels. An ultra-low power microcontroller controls on-board sensors and wireless protocol, to adapt the power consumption to the available detected power by changing wake-up policies. As a result, adaptive behavior can be observed in the designed platform, to the extent that the transmission data rate is dynamically determined by RF power. Among the novel features of the system, we highlight the use of nano-power energy harvesting, the implementation of specific hardware/software wake-up policies, optimized algorithms for best sampling rate implementation, and adaptive behavior by the node based on the power received.