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合并光子，电子和离子效应的新形式的神经形态系统，为内存计算和人工视觉记忆应用带来新的前景。这将在开发光电记忆中使用Coplanar Nanogap电极和多功能解决方案处理的材料，该材料与低面积的柔性基质相兼容。非成本工艺可以通过开发的电子设备来革新信息技术，从而通过开发的电子设备来实现现实效率的生物神经网络来革新信息技术。一个关键的组成部分是“人造突触”，需要高度可扩展和有效，同时支持类似于生物突触的丰富动力反应。这种平台的新兴应用是在神经形态的视觉中，其中光传感器不仅通过将光转换为电信号，而且还以极其有效的方式捕获并将有用的信息发送给处理单元，从而模仿人类视觉的时空性质。这与支持大量应用程序的实时模式识别任务尤其重要，从自主运动到护理点诊断，利用传感器的速度进步，更大的动态范围和计算成本降低。光遗传学领域率先使用了光敏蛋白，这些蛋白可以在照明时随意激活并刺激神经元发射。受这项技术的启发，我将制造可以通过光刺激控制的人造突触，与电气刺激相比，该突触可以在空间上限制，从而显着降低串扰和噪声，同时它们可以提高灵敏度和信号传播速度。我将采用一种简单的纳米化方法来设计与实际突触具有相同维度的原型设备，即宽度比纳米纳米纳米型分离的电极，纳米op在15 nm的范围内，类似于突触cle裂的大小。将使用粘附光刻技术来制造互连的纳米型电极模拟神经元网络，以应对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，我将利用光敏的多氧钙和卤化物钙钛矿来制造突触样金属/半导体/金属连接。这些材料及其与金属结构的界面形成膜的特性将被量身定制，以证明神经形态功能，例如（a）联想学习，（b）平行处理设备以模仿生物网络的稳态，并在阵列中启用光学刺激的空间整合，以启用阵列中的几种依据，以启用阵列的依据，以启用阵列的依据。基于横杆架构和仅电气刺激的现有回忆技术的优势。首先，Coplanar Nanogap电极由于其较低的尺寸而具有巨大的希望，可以实现低功耗和快速开关速度，正如已经使用其他类型的设备（射频频率二极管，光电探测器）所证明的，而平面的几何形状则促进了稳定的稳定性，并促进了驾驶员的范围，并逐渐消除了驾驶员的表现。其次，上述解决方案可配合的材料表现出许多有吸引力的光电特性，化学可调节性和制造性的优点，使它们适合于实现固定的性能目标。该奖学金的实施实施将代表范式的范式转移，以在基于UK的电子设备中建立Natan Interron，同时建立了Natan Me se n n n n n n n N. AI硬件。