All-Solid-State Synaptic Transistors with Lithium-Ion-Based Electrolytes for Linear Weight Mapping and Update in Neuromorphic Computing Systems.

Linear weights modulation in neuromorphic memristor plays an important role in high-accuracy image recognition task. Herein, a Li+ doped organic artificial memristor for neuromorphic computing was proposed for linear weights update, which shows active ions diffusive dynamics as Ca2+ diffusion in biological synapse. The memristor exhibits gradual resistive switching, multi-state storage and typical synaptic behaviors. In addition, the synaptic learning capability of letter “T” was demonstrated in a memristors array. By designing consecutive pulse waveforms with enhanced amplitude, the linearity of memristor for weight update in long-term potentiation and depression (LTP/LTD) could be improved from 6.8 to 0.4. Based on the great nonlinearity factor in LTP ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha _{\text {p}}={1.5}$ </tex-math></inline-formula> ) and LTD ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha _{\text {d}}={0.4}$ </tex-math></inline-formula> ), face recognition was achieved with high accuracy of 96% by artificial neural network consisting of ion doped memristors. The ion doped organic memristor with highly linear weights update provides guidelines for the development of bio-inspired ion diffusive neuromorphic computing system.

Owing to the controllable growth and large-area synthesis for high-density integration, interest in employing atomically thin two-dimensional (2D) transition-metal dichalcogenides (TMDCs) for synaptic transistors is increasing. In particular, substitutional doping of 2D materials allows flexible modulation of material physical properties, facilitating precise control in defect engineering for eventual synaptic plasticity. In this study, to increase the switch ratio of synaptic transistors, we selectively performed experiments on WS2 and introduced niobium (Nb) atoms to serve as the channel material. The Nb atoms were substitutionally doped at the W sites, forming a uniform distribution across the entire flakes. The synaptic transistor devices exhibited an improved switch ratio of 103, 100 times larger than that of devices prepared with undoped WS2. The Nb atoms in WS2 play crucial roles in trapping and detrapping electrons. The modulation of channel conductivity achieved through the gate effectively simulates synaptic potentiation, inhibition, and repetitive learning processes. The Nb-WS2 synaptic transistor achieves 92.30% recognition accuracy on the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset after 125 training iterations. This study’s contribution extends to a pragmatic and accessible atomic doping methodology, elucidating the strategies underlying doping techniques for channel materials in synaptic transistors.

Nanoscale channel organic ferroelectric synaptic transistor array for high recognition accuracy neuromorphic computing

Ferrimagnetic materials with antiparallel exchange coupling, exhibit spin‐orbit‐torque‐induced dynamics, offering an emerging platform for realizing neuromorphic devices, such as artificial synapses. However, the state‐of‐the‐art artificial synapses based on ferrimagnet suffer from poor analog switching linearity, which serves as a bottleneck for achieving complex tasks with high accuracy in neuromorphic computing. Here, an artificial synapse is reported with high‐weight update linearity in a compensated ferrimagnetic crossbar device. In particular, the linear weight update of the synapses is enhanced by engineering the current density distribution. Using experimentally derived device parameters, handwritten digit recognition can be achieved with an accuracy of over 95% in a three‐layer fully connected artificial neural network. The work provides a universal method to improve the synaptic linearity, which also paves the way for applying the spin‐orbit device in neuromorphic computing.

Synaptic devices based neuromorphic computing applications in artificial intelligence

This chapter explores relationships between the science and profession of psychology and the development of artificial intelligence (AI) and robotic systems. It briefly examines the role of psychology in the development of AI systems and theories. There is a long and intimate history of such a relationship, exemplified in the arts and the social sciences as well as in computer science and engineering. The chapter examines the role that psychology has played in the understanding of the ways in which humans interact with contemporary AI systems, to influence the behaviour of the systems and, in turn, to be influenced by them. Human decision making has been shown to be improved by factors other than intelligence and thereby appears to be a quality which would be retained by humans rather than machines, for example motivational processes, emotions and experience. The chapter examines the state of the profession of psychology and helping professions in the light of developments in AI.

The development of the digital revolution facilitates innovative models that generate new markets and business opportunities. The reappearance of artificial intelligence (AI) has created further potentials and types of market participation. AI is understood as a cutting-edge technology and a key driver of the transition of our economy into the digital economy. It is important to recognize and constantly bear in mind that artificial intelligence systems provide certain benefits but are associated with certain risks and potential negative effects. The European Commission, in its Ethical Guidelines for Trustworthy Artificial Intelligence (2019), emphasizes ethical principles and associated values that must be respected in the development, introduction, and use of artificial intelligence systems: respect for human autonomy, prevention of harm, fairness, and explainability. The question arises as to whether the emerging fundamental ethical principles and regulatory policies concerning AI systems require certain adaptations when it comes to the application of AI technology in connection with the sustainable development goals. The development of artificial intelligence systems compatible with the goals of sustainable development, as defined in the 1987 report of the UN Brundtland Commission as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs”, is encouraged. Sustainability or sustainable development is defined in the literature as a concept based on three pillars - encompassing social, economic, and environmental aspects. The European Commission highlights that sustainable development is a fundamental principle of the Union Treaty and a priority goal of Union policies, along with digitization and a robust single market. In recent years, doctrine has sought to bridge the gap between the two disciplines by introducing the term “Sustainable AI”. The aim of the paper is to understand the development of AI that is compatible with sustainable goals. To understand this, it is necessary to comprehend the basic concepts of artificial intelligence systems and explore the sociological, ecological, and economic implications of these systems, all with the aim of finding ways to achieve the goals of sustainable development and the sustainability of AI systems themselves. These are closely tied to adhering to the highest ethical principles with the responsible use of artificial intelligence systems.

Non-Von Neumann computing application constituted by artificial synapses based on electrochemical random-access memory (ECRAM) has aroused tremendous attention owing to its capability to perform parallel operations, thus reducing the cost of time and energy spent [1-3]. Existing ECRAM synapses comprise two-terminal memristors and three-terminal synaptic transistors (SynT). While low cost, scalability, and high density are the highlights for memristors, their nonlinear, asymmetric state modulation, high ON current withdrawal, and sneak path in crossbar array integration prevent them from becoming the ideal synaptic elements for artificial neural networks (ANN) [4]. SynT configuration, on the other hand, offers an additional electrolyte-gated control from which ion doping content can be monitored via redox reactions, thus decoupling write-read actions and improving the linearity of programming states [5-6]. Nevertheless, existing SynTs suffer from different integration issues stemming from liquid-based ionic conductors and manually exfoliated channels. Moreover, several kinds of SynTs possess highly conductive channels in the range of µS to mS, significantly scaling up the energy spent for analog states reading. Despite having numerous communications on the performance of different ECRAM, a comprehensive electrochemical view of ion intercalation into the active material, the main root of conductance modulation, is clearly missing.In this work, we present the elaboration procedure of an all-solid-state synaptic transistor composed of nanoscale electrolyte and channel layers. The devices have been elaborated on 8’’ Silicon wafers using microfabrication processes compatible with conventional semiconductor technology and CMOS back end of line (BEoL) integration. (Figure 1a)We demonstrate the excellent synaptic plasticity properties of short-term potentiation (STP) and long-term potentiation (LTP) of our SynT. We performed tests to study the correlation between linearity, asymmetry, and the number of analog states. By averaging the amount of injected ions per write operation, we estimated the energy consumed for switching among adjacent states of this device is 22.5 pJ, yielding area-normalized energy of 4 fJ/µm2. In addition, operating in the range of nS, our SynTs meet the critical criteria of low energy consumption for both write and read operations. Endurance was highlighted by cycling in ambient conditions with 100 states of potentiation and depression for over 1000 cycles with only a slight variation of Gmax/Gmin ratio of 6.2 % (Figure 1b, c). Approximately 95 % accuracy in MNIST pattern recognition test on ANN in the crossbar array configuration has been obtained by simulation with SynTs as synaptic elements reassured SynT is a promising candidate for future neuromorphic computing hardware.To shed light on the properties of intercalation phenomena of Li ions into the TiO2 layer, a further electrochemical study on a cell comprising Ti/TiO2/LiPON/Li corresponding to the SynT gate stack was performed. This understanding will help to elucidate the correlation with conductance modulation characteristics for a synaptic transistor. Multiple tests were carried out, including cyclic voltammetry (CV) with different scan rates, rate capability with Galvanostatic cycling with potential limit (GCPL), and electrochemical impedance spectroscopy (EIS) on different states of charge. A circuit model was introduced to fit the frequency response of the cell, and it explained well the behavior of charging capability at different OCV (Figure 1d).

Organic synaptic transistors: Biocompatible neuromorphic devices for in-vivo applications

Filamentary and interface switching of CMOS-compatible Ta2O5 memristor for non-volatile memory and synaptic devices

An oxygen-based ionic synaptic transistor (O-IST) is a promising synaptic element for neuromorphic computing. In this study, we demonstrated that the density of the electrolyte plays a key role in achieving excellent synaptic characteristics in an O-IST. In a Pr0.7Ca0.3MnO3-based O-IST, we precisely controlled the density of the HfOx electrolyte and found that a low-density electrolyte could improve the ion mobility. Owing to the improved ion mobility and controlled ion migration, we demonstrated that excellent synaptic characteristics, such as a wide dynamic range, linear weight update, low operating voltage operations, and stable cyclic operation, were achieved. Finally, we confirmed an improved pattern recognition accuracy using an O-IST with an HfOx electrolyte of optimal density.

"Background: Artificial Intelligence (AI) has become a ubiquitous phenomenon in recent times, with most organizations today attempting to maneuver their way around developing AI systems with the aim of improving the products and services they provide. However, what complicates developing AI systems is the paucity in frameworks to support organizations with AI System Development (SD). As a result, many organizations are using existing approaches which have been previously applied in earlier emerging Information Technology endeavors. This study explored how a framework can promote effective organizational AI SD. To achieve this a holistic framework for AI SD was conceptualized and examined from an organizational perspective. Method: This study has examined the conceptualized framework People-Process-Data-Technology (2PDT) in AI, through a case study research design. The empirical data was analyzed based on 12 case studies within Australia including 39 interviews with AI experts. We have applied thematic analysis to investigate requirements of organizational AI SD. Results: The results demonstrate that organizations are challenged by key factors, which inhibits their ability to effectively develop AI systems. For example, organizations are not achieving successful delivery of AI systems due to a lack of required skills. Additionally, a plethora of AI technology which is constantly evolving, poor data quality, and the paucity of AI SD frameworks are all contributing to unpredictable delivery outcomes. Conclusion: This paper investigated requirements for effective organizational AI SD by examining the 2PDT framework. The results contribute to AI phenomenon by developing the requirements for AI SD, in terms of people, process, data and technology. It contributes to theory by evaluating and developing AI requirements for effective AI systems. The examination of the framework and case study approach added valuable knowledge to the AI domain. In addition, we contributed to practice by identifying requirements that organizations should consider in achieving better AI SD outcomes."

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.

Analog in-memory computing synaptic devices are widely studied for efficient implementation of deep learning. However, synaptic devices based on resistive memory have difficulties implementing on-chip training due to the lack of means to control the amount of resistance change and large device variations. To overcome these shortcomings, silicon complementary metal-oxide semiconductor (Si-CMOS) and capacitor-based charge storage synapses are proposed, but it is difficult to obtain sufficient retention time due to Si-CMOS leakage currents, resulting in a deterioration of training accuracy. Here, a novel 6T1C synaptic device using only n-type indium gaIlium zinc oxide thin film transistor (IGZO TFT) with low leakage current and a capacitor is proposed, allowing not only linear and symmetric weight update but also sufficient retention time and parallel on-chip training operations. In addition, an efficient and realistic training algorithm to compensate for any remaining device non-idealities such as drifting references and long-term retention loss is proposed, demonstrating the importance of device-algorithm co-optimization.

The long-standing von Neumann architecture, while foundational to modern computing, intrinsically suffers from data-transfer inefficiency, which imposes severe limits on speed and energy efficiency in artificial intelligence, machine learning, and real-time data processing. Inspired by the remarkable energy efficiency of the human brain, neuromorphic computing seeks to emulate neural architectures through hardware capable of adaptive learning. Although early complementary metal-oxide-semiconductor (CMOS)-based neuromorphic systems captured basic synaptic behaviors, their narrow dynamic ranges and high operating voltages impede their application prospects. Van der Waals (vdW) materials, particularly tellurium (Te) and Selenium (Se), have recently emerged as promising platforms for next-generation neuromorphic devices due to their intriguing electronic and optoelectronic properties including considerable carrier mobilities, broadband photoresponse, and strong coupling between electrical, optical, and mechanical stimuli. These unique properties offer a direct physical analogy to biological synapses, thereby enabling neuromorphic computing. This Perspective introduces the fundamentals of synaptic behavior and neuromorphic computing and highlights the distinctive properties of Te/Se nanomaterials for synaptic devices. Then we discuss critical advances in Te/Se-based memristors, heterostructures, and electronic/optoelectronic synaptic transistors. In the end, we conclude this perspective with a discussion on the remaining challenges and future opportunities in this evolving field.