Neuromorphic Array Research Articles

Optoelectronic dual-synapse based on wafer-level GaN-on-Si device incorporating embedded SiO2 barrier layers

Optoelectronic synapses possess the potential to seamlessly integrate perception, storage, and neuromorphic computation, thereby offering advantages in constructing artificial vision systems. Gallium nitride (GaN) materials, renowned for their exceptional optoelectronic characteristics and the compatibility of Silicon-based GaN (GaN-on-Si) with CMOS technology, contribute significantly to realizing large-scale optoelectronic neuromorphic circuits. In this study, we present an optoelectronic dual-synapse based on a structurally-improved AlGaN/GaN MIS-HEMT with ring-shaped SiO2 barriers embedded surrounding the source and drain regions. This device facilitates simultaneous signal transmission to the source and drain through current-limiting electrical breakdown beneath the gate. This integration of two discrete optoelectronic synapses within a single device enhances neural signal transmission nodes and branching connectivity, facilitating the construction of large-scale neuromorphic circuits. Under the co-influence of electrical and optical pulses, the device not only demonstrates excellent optoelectronic synaptic characteristics but also exhibits the ability to modulate the critical wavelength of synaptic responses by varying the source/drain voltage, effectively simulating the light signal recognition observed in biological retinas. The synergistic operation of the two sub-synapses enables a successful transition from short-term potentiation (STP) to long-term potentiation (LTP). Furthermore, leveraging the device's STP, we emulate the function of artificial retina through a GaN optoelectronic neuromorphic array, successfully achieving image recognition in conjunction with artificial neural networks (ANNs). The devices developed in this study, based on GaN-on-Si allow wafer-level manufacturing, provding a promising avenue for the large-scale production of optoelectronic synapse devices and the realization of artificial vision systems.

Analysis of the spike responses in the neuromorphic implementation of the two-compartmental model of hippocampal pyramidal neuron

Designing a low-power neuromorphic circuit for the main units of the nervous system, neurons, plays a crucial role in neural engineering and neuroprosthesis. Due to the CA1 pyramidal neurons’ specific characteristics, these neurons have been a focus of research for several years. The Pinsky-Rinzel (P-R) model is a nonlinear two-compartmental model of the Hippocampal neuron that reveals various modes of firing patterns. In this paper, a low-power neuromorphic circuit for the Hippocampal P-R model is designed at 180 nm TSMC technology. Then, we analyze the recorded spiking responses from the neuroscience point of view. We also quantitatively compare the P-R model simulated in MATLAB with the proposed circuit in the Cadence simulator to show that the obtained responses are in good agreement. For the proposed circuit, the maximum energy consumption per spike is 97pJ/spike in the worst case. The effect of the device mismatch is also studied by performing a 200-point Monte Carlo simulation for different values of input currents. It is demonstrated that the proposed circuit is robust under process variation. Next, considering temporal coding, the ISI (inter-spike interval) and VP-d (Victor-Purpura distance) are extracted from the spike responses collected from the designed circuit. Finally, utilizing machine-learning algorithms, the applied input current to the circuit was accurately identified from the recorded spike trains. In this way, the spike patterns produced by the designed P-R circuit are diverse and consistent to be able to decode the input stimulus using typical machine learning classifiers. This supports the application of the proposed circuit in the hybrid neurocomputing systems and thus it can be integrated easily into large neuromorphic arrays in future works.

Highly adaptive and energy efficient neuromorphic computation enabled by deep-spike heterostructure photonic neuro-transistors

Recently, neuromorphic photonics using optical signal as a data domain are considered as a promising solution to realize the next generation neural network platform. Here, metal-chalcogenide/metal oxide semiconductor based photonic neuro-transistors with deep spike-like heterostructure are proposed as a highly adaptive and energy efficient neuromorphic device. In particular, the energy band structure of cadmium sulfide (CdS)/amorphous indium-gallium-zinc-oxide (a-IGZO) heterojunction is engineered via mediating the anion-to-cation ratio of CdS films. It is revealed that the S/Cd ratio is able to determine the work function of the film which consequently causes a variation in the degree of band-bending at the heterointerface. Using a CdS film with optimized S/Cd ratio (CdS1.2), deep spike-like heterostructure (DHS) can be constructed which enables efficient accumulation of photo-generated charge carriers and the emulation of biological synaptic functions including long-term potentiation (LTP) and depression (LTD) behaviors. Also, the a-IGZO/CdS1.2 DHS transistor exhibits low non-linearity value for LTP (1.1) and less energy consumption (45.04 pJ). Furthermore, 7 × 7 opteoelectronic neuromorphic arrays are successfully implemented to exhibit possibility of realization of hardware-based weight pixel training. In addition, the a-IGZO/CdS1.2 DHS transistor shows a high accuracy for image pattern recognition (85.96%) based on the artificial neural network simulation, proving the feasibility in the artificial intelligent systems.

Intrinsically stretchable neuromorphic devices for on-body processing of health data with artificial intelligence

<h2>Summary</h2> For leveraging wearable technologies to advance precision medicine, personalized and learning-based analysis of continuously acquired health data is indispensable, for which neuromorphic computing provides the most efficient implementation of artificial intelligence (AI) data processing. For realizing on-body neuromorphic computing, skin-like stretchability is required but has yet to be combined with the desired neuromorphic metrics, including linear symmetric weight update and sufficient state retention, for achieving high computing efficiency. Here, we report an intrinsically stretchable electrochemical transistor-based neuromorphic device, which provides a large number (>800) of states, linear/symmetric weight update, excellent switching endurance (>100 million), and good state retention (>10⁴ s) together with the high stretchability of 100% strain. We further demonstrate a prototype neuromorphic array that can perform vector-matrix multiplication even at 100% strain and also the feasibility of implementing AI-based classification of health signals with a high accuracy that is minimally influenced by the stretched state of the neuromorphic hardware.

Polydopamine‐Based All Solid‐State Flexible Organic Neuromorphic Devices for Access Device‐Free Artificial Neural Networks

AbstractRecent developments in organic neuromorphic devices and biohybrid interfaces are promising examples that show potential to improve implantable devices toward organic adaptive brain‐machine interfaces. However, fully integrated neuromorphic arrays still require relatively complex circuitry that includes multiple access devices to ensure synaptic weight stability and prevent sneak paths. Here, it is shown that polydopamine (PDA), the byproduct of dopamine autoxidation, promotes proton conductivity and can serve as a solid‐state electrolyte. Slow kinetics and high energy barriers of the PDA solid electrolyte prevent loss of conductance state for the device with a three‐terminal configuration without an access device, while partial dedoping of the conductive polymer channel by PDA simultaneously increases its stability in ambient environments. Fabricating the neuromorphic device on a flexible poly(styrene‐block‐isobutylene‐block‐styrene) substrate and the inherent biocompatibility of PDA demonstrates its potential toward more sophisticated implantable neuromorphic circuits for advanced neuroprosthetics.

Artificial neuromorphic cognitive skins based on distributed biaxially stretchable elastomeric synaptic transistors

Cephalopod (e.g., squid, octopus, etc.) skin is a soft cognitive organ capable of elastic deformation, visualizing, stealth, and camouflaging through complex biological processes of sensing, recognition, neurologic processing, and actuation in a noncentralized, distributed manner. However, none of the existing artificial skin devices have shown distributed neuromorphic processing and cognition capabilities similar to those of a cephalopod skin. Thus, the creation of an elastic, biaxially stretchy device with embedded, distributed neurologic and cognitive functions mimicking a cephalopod skin can play a pivotal role in emerging robotics, wearables, skin prosthetics, bioelectronics, etc. This paper introduces artificial neuromorphic cognitive skins based on arrayed, biaxially stretchable synaptic transistors constructed entirely out of elastomeric materials. Systematic investigation of the synaptic characteristics such as the excitatory postsynaptic current, paired-pulse facilitation index of the biaxially stretchable synaptic transistor under various levels of biaxial mechanical strain sets the operational foundation for stretchy distributed synapse arrays and neuromorphic cognitive skin devices. The biaxially stretchy arrays here achieved neuromorphic cognitive functions, including image memorization, long-term memorization, fault tolerance, programming, and erasing functions under 30% biaxial mechanical strain. The stretchy neuromorphic imaging sensory skin devices showed stable neuromorphic pattern reinforcement performance under both biaxial and nonuniform local deformation.

Progressive and Stable Synaptic Plasticity with Femtojoule Energy Consumption by the Interface Engineering of a Metal/Ferroelectric/Semiconductor.

In the era of “big data,” the cognitive system of the human brain is being mimicked through hardware implementation of highly accurate neuromorphic computing by progressive weight update in synaptic electronics. Low‐energy synaptic operation requires both low reading current and short operation time to be applicable to large‐scale neuromorphic computing systems. In this study, an energy‐efficient synaptic device is implemented comprising a Ni/Pb(Zr0.52Ti0.48)O3 (PZT)/0.5 wt.% Nb‐doped SrTiO3 (Nb:STO) heterojunction with a low reading current of 10 nA and short operation time of 20–100 ns. Ultralow femtojoule operation below 9 fJ at a synaptic event, which is comparable to the energy required for synaptic events in the human brain (10 fJ), is achieved by adjusting the Schottky barrier between the top electrode and ferroelectric film. Moreover, progressive domain switching in ferroelectric PZT successfully induces both low nonlinearity/asymmetry and good stability of the weight update. The synaptic device developed here can facilitate the development of large‐scale neuromorphic arrays for artificial neural networks with low energy consumption and high accuracy.

Stretchable Temperature-Responsive Multimodal Neuromorphic Electronic Skin with Spontaneous Synaptic Plasticity Recovery.

Multimodal electronic skin devices capable of detecting multimodal signals provide the possibility for health monitoring. Sensing and memory for temperature and deformation by human skin are of great significance for the perception and monitoring of physiological changes of the human body. Electronic skin is highly expected to have similar functions as human skin. Here, by implementing intrinsically stretchable neuromorphic transistors with mechanoreceptors and thermoreceptors in an array, we have realized stretchable temperature-responsive multimodal neuromorphic electronic skin (STRM-NES) with both sensory and memory functions, in which synaptic plasticity can be modulated by multiple modalities, in situ temperature variations, and stretching deformations. Temperature-responsive functions, spontaneous recovery, and temperature-dependent multitrial learning are proposed. Furthermore, a stretchable temperature neuromorphic array composed of multiple fully functional subcells is demonstrated to identify temperature distributions and variations at different regions and conditions after various strains of skin. The STRM-NES has temperature- and strain-responsive neuromorphic functions, excellent self-healing, and reusable capability, showing similar abilities as human skin to sense, transmit, memory, and recovery from external stimuli. It is expected to facilitate the development of wearable electronics, intelligent robotics, and prosthetic applications.

Adaptive Biosensing and Neuromorphic Classification Based on an Ambipolar Organic Mixed Ionic-Electronic Conductor.

Organic mixed ionic-electronic conductors (OMIECs) are central to bioelectronic applications such as biosensors, health-monitoring devices, and neural interfaces, and have facilitated efficient next-generation brain-inspired computing and biohybrid systems. Despite these examples, smart and adaptive circuits that can locally process and optimize biosignals have not yet been realized. Here, a tunable sensing circuit is shown that can locally modulate biologically relevant signals like electromyograms (EMGs) and electrocardiograms (ECGs), that is based on a complementary logic inverter combined with a neuromorphic memory element, and that is constructed from a single polymer mixed conductor. It is demonstrated that a small neuromorphic array based on this material effects high classification accuracy in heartbeat anomaly detection. This high-performance material allows for straightforward monolithic integration, which reduces fabrication complexity while also achieving high on/off ratios with excellent ambient p- and n-type stability in transistor performance. This material opens a route toward simple and straightforward fabrication and integration of more sophisticated adaptive circuits for future smart bioelectronics.

Ultrahigh drive current and large selectivity in GeS selector

Selector devices are indispensable components of large-scale nonvolatile memory and neuromorphic array systems. Besides the conventional silicon transistor, two-terminal ovonic threshold switching device with much higher scalability is currently the most industrially favored selector technology. However, current ovonic threshold switching devices rely heavily on intricate control of material stoichiometry and generally suffer from toxic and complex dopants. Here, we report on a selector with a large drive current density of 34 MA cm−2 and a ~106 high nonlinearity, realized in an environment-friendly and earth-abundant sulfide binary semiconductor, GeS. Both experiments and first-principles calculations reveal Ge pyramid-dominated network and high density of near-valence band trap states in amorphous GeS. The high-drive current capacity is associated with the strong Ge-S covalency and the high nonlinearity could arise from the synergy of the mid-gap traps assisted electronic transition and local Ge-Ge chain growth as well as locally enhanced bond alignment under high electric field.

Perspective: Uniform switching of artificial synapses for large-scale neuromorphic arrays

Resistive random-access memories are promising analog synaptic devices for efficient bio-inspired neuromorphic computing arrays. Here we first describe working principles for phase-change random-access memory, oxide random-access memory, and conductive-bridging random-access memory for artificial synapses. These devices could allow for dense and efficient storage of analog synapse connections between CMOS neuron circuits. We also discuss challenges and opportunities for analog synaptic devices toward the goal of realizing passive neuromorphic computing arrays. Finally, we focus on reducing spatial and temporal variations, which is critical to experimentally realize powerful and efficient neuromorphic computing systems.

A programmable spike-timing based circuit block for reconfigurable neuromorphic computing

A generic programmable spike-timing based circuit which forms the building block of a reconfigurable neuromorphic array is implemented in analog VLSI. An array of programmable spike time event coded circuit blocks is configured to implement functional circuit blocks of a spike time based neuromorphic model. A reconfigurable neuromorphic array chip with 10 event blocks is fabricated using Austria Microsystems 0.35 μ m CMOS technology to demonstrate the functionality of the circuits in silicon.