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.

该项目的目标是开发一种融合光子、电子和离子效应的新型神经形态系统，为内存计算和人工视觉记忆的应用带来新的前景。这将通过开发光电存储器来实现，该存储器采用共面纳米间隙电极和多功能溶液加工材料，采用与大面积柔性衬底兼容的低成本工艺制造。神经形态工程准备通过开发能够真实地模拟生物神经网络的电子设备来彻底改变信息技术。一个关键的组成部分是“人工突触”，它需要高度可扩展和节能，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在神经形态视觉中，其中光传感器不仅通过将光转换为电信号，而且通过以极其有效的方式捕获并将有用的信息发送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，从自主运动到即时诊断，利用传感器在速度、更大动态范围和更低计算成本方面的进步。光遗传学领域率先使用了光敏蛋白，这种蛋白可以在光照下随意激活，刺激神经元放电。受此技术的启发，我将制造可由光刺激控制的人造突触，与电刺激相比，它可以在空间上受限，从而大大减少串扰和噪声，同时它们具有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器件，即大宽高比纳米间隙分离电极，纳米间隙在15 nm范围内，类似于突触间隙的大小。为了解决目前在大面积柔性基板上可靠制造纳米结构的挑战，将使用附着光刻技术制造模拟神经网络的互连纳米间隙电极。最后，我将使用光敏多金属氧酸盐和卤化物钙钛矿来制造类似突触的金属/半导体/金属结。这些材料的成膜特性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)设备的并行寻址以模拟生物网络的稳态，以及(c)阵列中光刺激的空间集成，以根据每个像素的光强度/波长实现选择性存储。我的方法比现有的记忆技术有几个优点，这些技术是基于交叉结构和单独的电刺激。首先，共面纳米间隙电极，由于其低维度，具有实现低功耗和快速开关速度的巨大希望，正如其他类型的器件（射频二极管，光电探测器）所证明的那样，而其平面几何形状有利于光控操作，既可以模拟调整电阻状态，又可以消除阵列配置中的潜电流。其次，上述溶液可加工材料具有许多吸引人的光电特性，化学可调性和可制造性优点，使其适合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设备制造的范式转变，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级光电子领域的领导者。

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