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

基本信息

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

项目摘要

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加工材料的光电存储器来实现，这些材料采用与大面积柔性基板兼容的低成本工艺制造。真实地模拟生物神经网络。一个关键组件是“人工突触”，它需要具有高度可扩展性和功效，同时支持类似于生物突触的丰富动态响应。此类平台的一个新兴应用是神经形态视觉，其中光传感器模仿人类视觉的时空性质，不仅将光转换为电信号，而且还以极其高效的方式捕获仅有用的信息并将其发送到处理单元。方式。这对于支持大量应用的实时模式识别任务尤其重要，从自主运动到护理点诊断，利用传感器在速度方面的进步、更大的动态范围和更低的计算成本。光遗传学领域率先使用光敏蛋白，这种蛋白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，我将制造可以通过光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加工方法来设计与实际突触相同维度的原型器件，即大纵横比纳米间隙分离电极，纳米间隙在15纳米范围内，与突触间隙的大小相似。将使用粘附光刻技术来制造模拟神经网络的互连纳米间隙电极，以解决当前在大面积柔性基板上可靠制造纳米级结构的挑战。最后，我将采用光敏多金属氧酸盐和卤化物钙钛矿来制造类突触的金属/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）空间整合阵列中的光刺激可以根据每个像素上的光强度/波长实现选择性存储。我的方法比现有的忆阻技术具有多个优势，现有的忆阻技术基于交叉架构和单独的电刺激。首先，共面纳米间隙电极由于其低维度，有望实现低功耗和快速开关速度，正如其他类型的设备（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，实现电阻状态的模拟调谐并消除阵列配置中的潜行电流。其次，上述可溶液加工材料具有许多有吸引力的光电特性、化学可调性和可制造性优点，使它们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，为英国提供支持基于电子和制造业，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。