Characterization and Programming Algorithm of Phase Change Memory Cells for Analog In-Memory Computing.

In this paper, a thorough characterization of phase-change memory (PCM) cells was carried out, aimed at evaluating and optimizing their performance as enabling devices for analog in-memory computing (AIMC) applications. Exploiting the features of programming pulses, we discuss strategies to reduce undesired phenomena that afflict PCM cells and are particularly harmful in analog computations, such as low-frequency noise, time drift, and cell-to-cell variability of the conductance. The test vehicle is an embedded PCM (ePCM) provided by STMicroelectronics and designed in 90-nm smart power BCD technology with a Ge-rich Ge-Sb-Te (GST) alloy for automotive applications. On the basis of the results of the characterization of a large number of cells, we propose an iterative algorithm to allow multi-level cell conductance programming, and its performances for AIMC applications are discussed. Results for a group of 512 cells programmed with four different conductance levels are presented, showing an initial conductance spread under 6%, relative current noise less than 9% in most cases, and a relative conductance drift of 15% in the worst case after 14 h from the application of the programming sequence.

A Long-Distance RF-Powered Sensor Node with Adaptive Power Management for IoT Applications.

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.

EEG acquisition system based on active electrodes with common-mode interference suppression by Driving Right Leg circuit.

We present a system for the acquisition of EEG signals based on active electrodes and implementing a Driving Right Leg circuit (DgRL). DgRL allows for single-ended amplification and analog-to-digital conversion, still guaranteeing a common mode rejection in excess of 110 dB. This allows the system to acquire high-quality EEG signals essentially removing network interference for both wet and dry-contact electrodes. The front-end amplification stage is integrated on the electrode, minimizing the system's sensitivity to electrode contact quality, cable movement and common mode interference. The A/D conversion stage can be either integrated in the remote back-end or placed on the head as well, allowing for an all-digital communication to the back-end. Noise integrated in the band from 0.5 to 100 Hz is comprised between 0.62 and 1.3 µV, depending on the configuration. Current consumption for the amplification and A/D conversion of one channel is 390 µA. Thanks to its low noise, the high level of interference suppression and its quick setup capabilities, the system is particularly suitable for use outside clinical environments, such as in home care, brain-computer interfaces or consumer-oriented applications.

Active electrode IC for EEG and electrical impedance tomography with continuous monitoring of contact impedance.

The IC presented integrates the front-end for EEG and Electrical Impedance Tomography (EIT) acquisition on the electrode, together with electrode-skin contact impedance monitoring and EIT current generation, so as to improve signal quality and integration of the two techniques for brain imaging applications. The electrode size is less than 2 cm(2) and only 4 wires connect the electrode to the back-end. The readout circuit is based on a Differential Difference Amplifier and performs single-ended amplification and frequency division multiplexing of the three signals that are sent to the back-end on a single wire which also provides power supply. Since the system's CMRR is a function of each electrode's gain accuracy, an analysis is performed on how this is influenced by mismatches in passive and active components. The circuit is fabricated in 0.35 µm CMOS process and occupies 4 mm(2), the readout circuit consumes 360 µW, the input referred noise for bipolar EEG signal acquisition is 0.56 µVRMS between 0.5 and 100 Hz and almost halves if only EEG signal is acquired.