An adaptive synaptic array using Fowler-Nordheim dynamic analog memory.

In this paper we present an adaptive synaptic array that can be used to improve the energy-efficiency of training machine learning (ML) systems. The synaptic array comprises of an ensemble of analog memory elements, each of which is a micro-scale dynamical system in its own right, storing information in its temporal state trajectory. The state trajectories are then modulated by a system level learning algorithm such that the ensemble trajectory is guided towards the optimal solution. We show that the extrinsic energy required for state trajectory modulation can be matched to the dynamics of neural network learning which leads to a significant reduction in energy-dissipated for memory updates during ML training. Thus, the proposed synapse array could have significant implications in addressing the energy-efficiency imbalance between the training and the inference phases observed in artificial intelligence (AI) systems.

In Situ Grown Gold Nanoisland-Based Chemiresistive Electronic Nose for Sniffing Distinct Odor Fingerprints.

Chemiresistors based on metal-insulator-metal structures are attractive transducers for rapid tracing of a wide repertoire of (bio)chemical species in the vapor phase. However, current fabrication techniques suffer greatly from sensor-to-sensor variability, limiting their reproducible and reliable application in real-world settings. We demonstrate a novel, facile, and ubiquitously applicable strategy for fabricating highly reliable and reproducible organothiol-functionalized gold nanoisland-based chemiresistors. The novel fabrication technique involves iterative in situ seeding, growth, and surface functionalization of gold nanoislands on an interdigitated electrode, which in turn generates a multi-layered densely packed continuous gold nanoisland film. The chemiresistors fabricated using the proposed strategy exhibited high sensor-to-sensor reproducibility owing to the controlled iterative seeding and growth-based fabrication technique, long-term stability, and specificity for detection and identification of a wide variety of volatile organic compounds. Upon exposure to a specific odor, the chemiresistor ensemble comprised nine different chemical functionalities and produced a unique and discernable odor fingerprint that is reproducible for at least up to 90 days. Integrating these odor fingerprints with a simple linear classifier was found to be sufficient for discriminating between all six odors used in this study. We believe that the fabrication strategy presented here, which is agnostic to chemical functionality, enables fabrication of highly reliable and reproducible sensing elements, and thereby an adaptable electronic nose for a wide variety of real-world gas sensing applications.

Studying the Feasibility of Postoperative Monitoring of Spinal Fusion Progress Using a Self-Powered Fowler-Nordheim Sensor-Data-Logger.

OBJECTIVE: This study investigates the feasibility of using a new self-powered sensing and data logging system for postoperative monitoring of spinal fusion progress. The proposed diagnostic technology directly couples a piezoelectric transducer signal into a Fowler-Nordheim (FN) quantum tunneling-based synchronized dynamical system to record the mechanical usage of spinal fixation devices. The operation of the proposed implantable FN sensor-data-logger is completely self-powered by harvesting the energy from the micro-motion of the spine during the course of fusion. Bench-top testing is performed using corpectomy models to evaluate the performance of the proposed monitoring system. In order to simulate the spinal fusion process, different materials with gradually increasing elastic modulus are used to fill the intervertebral space gap. Besides, finite element models are developed to analyze the strains induced on the spinal rods during the applied cyclic loading. Data measured from the benchtop experiment is processed using an FN sensor-data-logger model to obtain time-evolution curves representing each spinal fusion state. This feasibility study shows that the obtained curves are viable tools to differentiate between conditions of osseous union and assess the effective fusion period.

A self-powered analog sensor-data-logging device based on Fowler-Nordheim dynamical systems.

Continuous, battery-free operation of sensor nodes requires ultra-low-power sensing and data-logging techniques. Here we report that by directly coupling a sensor/transducer signal into globally asymptotically stable monotonic dynamical systems based on Fowler-Nordheim quantum tunneling, one can achieve self-powered sensing at an energy budget that is currently unachievable using conventional energy harvesting methods. The proposed device uses a differential architecture to compensate for environmental variations and the device can retain sensed information for durations ranging from hours to days. With a theoretical operating energy budget less than 10 attojoules, we demonstrate that when integrated with a miniature piezoelectric transducer the proposed sensor-data-logger can measure cumulative "action" due to ambient mechanical acceleration without any additional external power.

A Spiking Neuron and Population Model Based on the Growth Transform Dynamical System.

In neuromorphic engineering, neural populations are generally modeled in a bottom-up manner, where individual neuron models are connected through synapses to form large-scale spiking networks. Alternatively, a top-down approach treats the process of spike generation and neural representation of excitation in the context of minimizing some measure of network energy. However, these approaches usually define the energy functional in terms of some statistical measure of spiking activity (ex. firing rates), which does not allow independent control and optimization of neurodynamical parameters. In this paper, we introduce a new spiking neuron and population model where the dynamical and spiking responses of neurons can be derived directly from a network objective or energy functional of continuous-valued neural variables like the membrane potential. The key advantage of the model is that it allows for independent control over three neuro-dynamical properties: (a) control over the steady-state population dynamics that encodes the minimum of an exact network energy functional; (b) control over the shape of the action potentials generated by individual neurons in the network without affecting the network minimum; and (c) control over spiking statistics and transient population dynamics without affecting the network minimum or the shape of action potentials. At the core of the proposed model are different variants of Growth Transform dynamical systems that produce stable and interpretable population dynamics, irrespective of the network size and the type of neuronal connectivity (inhibitory or excitatory). In this paper, we present several examples where the proposed model has been configured to produce different types of single-neuron dynamics as well as population dynamics. In one such example, the network is shown to adapt such that it encodes the steady-state solution using a reduced number of spikes upon convergence to the optimal solution. In this paper, we use this network to construct a spiking associative memory that uses fewer spikes compared to conventional architectures, while maintaining high recall accuracy at high memory loads.

Differential Fowler-Nordheim Tunneling Dynamical System for Attojoule Sensing and Recording.

Dynamical systems that evolve unidirectionally with respect to time provide a natural mechanism for implementing a time-domain, near-zero-threshold energy rectifier. In this paper we implement such a dynamical system using a pair of differential, leaky floating-gates and demonstrate that the circuit can sense and record signals of interest while compensating for environmental variations. A Fowler-Nordheim (FN) tunneling current has been used to implement the leakage process, which we experimentally show can be modulated by signals at energy levels below femtojoules. At this level of energy, the proposed FN-system could be self-powered using different types of biopotential energy sources like intra-cellular potentials, a feature that was not possible with previously reported recorders. Furthermore, the degree of modulation is shown to be a function of the input intensity as well as time-of-occurrence, which opens up the possibility of using reconstruction techniques to reconstruct the input signal from measurement of multiple sensing devices. Using devices fabricated in a 0.5 µm standard CMOS process, we demonstrate recording of 6 mV events with retention capability lasting over 30 minutes.