Thermally controlled electrochemical CMOS microsystem for protein array biosensors

Embodied neuromorphic intelligence

The design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building compact and low-power processing systems. We discuss why endowing robots with neuromorphic technologies - from perception to motor control - represents a promising approach for the creation of robots which can seamlessly integrate in society. We present initial attempts in this direction, highlight open challenges, and propose actions required to overcome current limitations.

Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

Spiking neural networks (SNN) are computational models inspired by the brain’s ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the conventional artificial neural networks, though their full computational capabilities are yet to be explored. Recently, in-memory computing architectures based on non-volatile memory crossbar arrays have shown great promise to implement parallel computations in artificial and spiking neural networks. In this work, we evaluate the feasibility to realize high-performance event-driven in-situ supervised learning systems using nanoscale and stochastic analog memory synapses. For the first time, the potential of analog memory synapses to generate precisely timed spikes in SNNs is experimentally demonstrated. The experiment targets applications which directly integrates spike encoded signals generated from bio-mimetic sensors with in-memory computing based learning systems to generate precisely timed control signal spikes for neuromorphic actuators. More than 170,000 phase-change memory (PCM) based synapses from our prototype chip were trained based on an event-driven learning rule, to generate spike patterns with more than 85% of the spikes within a 25 ms tolerance interval in a 1250 ms long spike pattern. We observe that the accuracy is mainly limited by the imprecision related to device programming and temporal drift of conductance values. We show that an array level scaling scheme can significantly improve the retention of the trained SNN states in the presence of conductance drift in the PCM. Combining the computational potential of supervised SNNs with the parallel compute power of in-memory computing, this work paves the way for next-generation of efficient brain-inspired systems.

Design of a wideband CMOS impedance spectroscopy ASIC analog front-end for multichannel biosensor interfaces

This paper presents the preliminary design and simulation of a flexible and programmable analog front-end (AFE) circuit with current and voltage readout capabilities for electric impedance spectroscopy (EIS). The AFE is part of a fully integrated multifrequency EIS platform. The current readout comprises of a transimpedance stage and an automatic gain control (AGC) unit designed to accommodate impedance changes larger than 3 order of magnitude. The AGC is based on a dynamic peak detector that tracks changes in the input current over time and regulates the gain of a programmable gain amplifier in order to optimise the signal-to-noise ratio. The system works up to 1 MHz. The voltage readout consists of a 2 stages of fully differential current-feedback instrumentation amplifier which provide 100 dB of CMRR and a programmable gain up to 20 V/V per stage with a bandwidth in excess of 10MHz.