Optogenetics-inspired photoelectric memories based on flexible nanogap electrodes

基于柔性纳米间隙电极的光遗传学启发光电存储器

• 批准号: MR/V024442/1
• 负责人: Dimitra Georgiadou
• 金额: $ 162.09万
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FV024442%2F1
• 关键词: Optogenetics; inspired; photoelectric; memories; based

The aim of this project is to develop a new form of neuromorphic systems that merge photonic, electronic and ionic effects, bringing new prospects for in-memory computing and artificial visual memory applications. This will be achieved upon developing photoelectric memories that employ coplanar nanogap electrodes and multi-functional solution-processed materials, fabricated with low-cost processes compatible with large-area flexible substrates.Neuromorphic engineering is poised to revolutionise information technologies by developing electronic devices that can realistically emulate biological neural networks. A key component is the "artificial synapse" that needs to be highly scalable and power efficient, whilst supporting rich dynamical responses akin to biological synapses. An emerging application of such platforms is in neuromorphic vision, where light sensors mimic the spatio-temporal nature of human vision not only by turning light into electrical signals but also by capturing and sending the useful-only information to the processing unit in an extremely efficient manner. This is particularly relevant for real-time pattern recognition tasks that support a plethora of applications, from autonomous locomotion to point-of-care diagnostics, leveraging the sensors advances in speed, greater dynamic range and decreased computational cost. The field of optogenetics has pioneered the use of light-sensitive proteins that can be activated at will upon illumination and stimulate the neurons to fire. Inspired by this technology, I will fabricate artificial synapses that can be controlled by optical stimuli, which, in contrast to electrical ones, can be spatially confined reducing thus significantly the crosstalk and noise, while they enable higher sensitivity and signal propagation speed. I will employ a simple nanofabrication method to design prototype devices of the same dimensionality as the actual synapse, namely large aspect ratio nanogap-separated electrodes, the nanogap being in the range of 15 nm, similar to the size of the synaptic cleft. Interconnected nanogap electrodes emulating neuronal networks will be fabricated using adhesion lithography technique to address the current challenge of reliable manufacturing of nanoscale structures on large area flexible substrates. Finally, I will employ photosensitive polyoxometalate and halide perovskite to fabricate synaptic-like metal/semiconductor/metal junctions. The film forming properties of these materials and their interfaces with the metal structures will be tailored to demonstrate neuromorphic functionalities, such as (a) associative learning, (b) parallel addressing of devices to emulate homeostasis of biological networks and (c) spatial integration of the optical stimulus in the array to enable selective storage depending on the light intensity/wavelength on each pixel.My approach presents several advantages over the existing memristive technologies, which are based on crossbar architectures and solely electrical stimulus. First, coplanar nanogap electrodes, owing to their low dimensionality, hold great promise for achieving low power consumption and fast switching speeds, as already demonstrated with other types of devices (radiofrequency diodes, photodetectors), while their planar geometry facilitates a light-controlled operation, enabling both analogue tuning of resistance states and elimination of sneak currents in the array configuration. Second, the aforementioned solution-processable materials present many attractive optoelectronic properties, chemical tunability and manufacturability merits that render them suitable to reach the set performance goals.Successful implementation of this fellowship will represent a paradigm shift in the fabrication of neuromorphic devices, supporting the UK-based electronics and manufacturing industry, while it will establish me as a leader in the field of nanoscale optoelectronics for AI hardware.

该项目的目的是开发一种新形式的神经形态系统，融合光子，电子和离子效应，为内存计算和人工视觉记忆应用带来新的前景。这将通过开发采用共面纳米间隙电极和多功能溶液处理材料的光电存储器来实现，该光电存储器采用与大面积柔性基板兼容的低成本工艺制造。神经形态工程有望通过开发能够真实模拟生物神经网络的电子设备来彻底改变信息技术。一个关键的组件是“人工突触”，它需要高度可扩展和功率效率，同时支持类似于生物突触的丰富动态响应。这种平台的一个新兴应用是神经形态视觉，其中光传感器不仅通过将光转化为电信号，而且还通过以极其有效的方式捕获并发送仅有用的信息到处理单元来模仿人类视觉的时空性质。这对于支持大量应用的实时模式识别任务尤其重要，从自主运动到床旁诊断，利用传感器在速度、更大的动态范围和更低的计算成本方面的进步。光遗传学领域开创了使用光敏蛋白质的先河，这些蛋白质可以在光照下随意激活，并刺激神经元放电。受这项技术的启发，我将制造可以由光学刺激控制的人工突触，与电刺激相比，它可以在空间上受到限制，从而显着减少串扰和噪声，同时它们可以实现更高的灵敏度和信号传播速度。我将采用一种简单的纳米制造方法来设计与实际突触相同维度的原型设备，即大纵横比纳米间隙分离电极，纳米间隙在15 nm的范围内，类似于突触间隙的大小。互连的纳米间隙电极模拟神经网络将使用粘附光刻技术，以解决目前的挑战，可靠的制造大面积柔性基板上的纳米结构。最后，我将使用光敏聚氧乙烯酸盐和卤化物钙钛矿来制造突触状金属/半导体/金属结。这些材料的成膜性质及其与金属结构的界面将被定制以展示神经形态功能，例如（a）联想学习，（B）装置的并行寻址以模拟生物网络的稳态，以及（c）阵列中的光学刺激的空间整合，以使得能够根据光强度/光强进行选择性存储。我的方法呈现了优于现有忆阻技术的几个优点，现有忆阻技术基于交叉结构和单独的电刺激。首先，共面纳米间隙电极，由于它们的低维度，对于实现低功耗和快速切换速度具有很大的希望，如已经用其他类型的设备（射频二极管、光电探测器）所证明的，而它们的平面几何形状促进光控操作，使得能够模拟调谐电阻状态和消除阵列配置中的寄生电流。第二，上述可溶液加工的材料具有许多吸引人的光电特性、化学可调谐性和可制造性优点，使它们适合于达到设定的性能目标。该奖学金的成功实施将代表神经形态器件制造的范式转变，支持英国的电子和制造业，同时，它将使我成为人工智能硬件纳米光电领域的领导者。

项目成果

High On/Off Ratio Carbon Quantum Dot-Chitosan Biomemristors with Coplanar Nanogap Electrodes

• DOI: 10.1021/acsaelm.2c00979
• 发表时间: 2022-12-21
• 期刊: ACS APPLIED ELECTRONIC MATERIALS
• 影响因子: 4.7
• 作者: Raeis-Hosseini, Niloufar;Georgiadou, Dimitra G.;Papavassiliou, Christos
• 通讯作者: Papavassiliou, Christos

2.11 - Accurate characterization of indoor photovoltaic performance.

• DOI: 10.1088/2515-7639/acc550
• 发表时间: 2023
• 期刊: JPhys materials

Microwave-Enabled Wearables: Underpinning Technologies, Integration Platforms, and Next-Generation Roadmap

• DOI: 10.1109/jmw.2022.3223254
• 发表时间: 2023-01-01
• 期刊: IEEE JOURNAL OF MICROWAVES
• 作者: Wagih，Mahmoud;Balocchi，Leonardo;Beeby，Steve
• 通讯作者: Beeby，Steve

Towards Solution-Processed RF Rectennas: Experimental Characterization and Non-Linear Modelling based on ZnO Nanogap Diodes

迈向解决方案处理的射频整流天线：基于 ZnO 纳米间隙二极管的实验表征和非线性建模

• DOI: 10.1109/icecs202256217.2022.9971051
• 发表时间: 2022
• 作者: Wagih M

Advances in Organic and Perovskite Photovoltaics Enabling a Greener Internet of Things

• DOI: 10.1002/adfm.202200694
• 发表时间: 2022-04
• 期刊: Advanced Functional Materials
• 影响因子: 19
• 作者: Julianna Panidi;D. Georgiadou;T. Schoetz;T. Prodromakis