Designing Neuromorphic 3D-Circuits using Memristors

Recently, I discovered a new interesting research area: neuromorphic circuits. Seems like the most interesting electronic circuit component used for designing neuromorphic circuits is the memristor, known as being the 4th electronic circuit component near the resistor, capacitor, and inductor. Its first concept was proposed theoretically in 1971 by Professor Leon Chua at the University of California, Berkeley, and physically realized for the first time only in 2008 by HP.

I remember knowing some ex-colleague of mine from the SRH Hochschule Heidelberg when I was doing an Erasmus semester there in 2013, which was doing some basic experiments with memristors for his Master thesis, but I didn’t take it too seriously at that time, because it was relatively new circuit component, and I was more busy with the software side (web design) that time. Despite some knowledge in AI developed during my Ph.D. and Post-Doc, I was always playing with the software side of AI but never implemented neuromorphic circuits, especially not ones with resistive devices (memristors). These days I was reading some research papers about how one can design some basic neuromorphic circuits using memristors and got a headache. I also installed LTspice, which seems like it is used for simulating simple neuromorphic circuit prototypes (also Cadence Virtuoso I saw being used often). I remember I had it installed in 2013 in Heidelberg, but never since due to me being a non-visionary at that time, haha.

A guy who does research in this field told me „For the circuits, I use LTSPice with compact models of memristors for prototyping. And then I use Cadence Analog mixed signals design toolchain to use the PDK and then transfer and fine-tune the transistor size etc.“ Also, „in order to stack them vertically, seems like if you want to have a working prototype, currently the only option is the Skywater 130nm process from Google„. Oh, Tesla, where are you?! I cry inside.

Below, I will post some links (I will add more links soon) in case you are also interested in knowing more about this topic:

Resistive Switching Devices for Neuromorphic Computing: From Foundations to Chip Level Innovations –

3D integration of 2D electronics

Hardware implementation of memristor-based artificial neural networks

Memristors and Memelements

Neuromorphic 3D-Circuits – Google Suche –

Three-dimensional hybrid circuits: the future of neuromorphic computing hardware –

Three-dimensional memristor circuits as complex neural networks –

(1) (PDF) Neuromorphic 3D Integrated Circuit: A Hybrid, Reliable and Energy Efficient Approach for Next Generation Computing –

Monolithic 3D neuromorphic computing system with hybrid CMOS and memristor-based synapses and neurons – ScienceDirect –

2103.04852.pdf –

Three-Dimensional Neuromorphic Computing System With Two-Layer and Low-Variation Memristive Synapses | IEEE Journals & Magazine | IEEE Xplore –

3D Neuromorphic Hardware with Single Thin‐Film Transistor Synapses Over Single Thin‐Body Transistor Neurons by Monolithic Vertical Integration – PMC –

Fully Integrated Memristor System for Neuromorphic and Analog Computing –

PowerPoint Presentation –

TODAES2501-08 –

3D-Integrated Neuromorphic Hardware With A Two-Level Neuromorphic “Synapse Over Neuron” Structure –

Brain-derived neuromorphic computing with 3D electronic-photonic integrated circuits| Yoo | Publications | Spie –

3D neuromorphic circuits | Electrical and Electronic Engineering Community –

Bio‐Inspired 3D Artificial Neuromorphic Circuits – Liu – 2022 – Advanced Functional Materials – Wiley Online Library –

Explore Neuromorphic Computing: Community, Education, and Research –

sandialabs/cross-sim: CrossSim: accuracy simulation of analog in-memory computing –

Microsoft Word – Panzeri_preprint_post.docx –

Journal of Circuits, Systems and Computers –

The Roadmap to Realize Memristive Three-Dimensional Neuromorphic Computing System | IntechOpen –

Materials | Free Full-Text | Memristors for Neuromorphic Circuits and Artificial Intelligence Applications –

ICCAD 2023 – Power-Aware Training for Energy-Efficient Printed Neuromorphic CIrcuits – YouTube –

Energy Efficient AI Hardware: neuromorphic circuits and tools – YouTube –

Machine learning using magnetic stochastic synapses – IOPscience –

memristor explained – YouTube –

Memristor devices ( presentation) – YouTube –

Neuromorphic Computing Explained | Jeffrey Shainline and Lex Fridman – YouTube –

neuromorphic computing – YouTube –

Brain-Like (Neuromorphic) Computing – Computerphile – YouTube –

Bio-inspired Computing with Memristors – YouTube –

A review of memristor: material and structure design, device performance, applications and prospects – PMC –

Frontiers | Overview of Memristor-Based Neural Network Design and Applications –

3D Convolutional Neural Network based on memristor for video recognition – ScienceDirect –

Day-14_Video-1 Neuromorphic Computing by Prof. Shubham Sahay – YouTube –

Flexible Simulation for Neuromorphic Circuit Design: Motion Detection Case Study –

Neuromorphic Hardware –

Handbook of Memristor Networks | SpringerLink –

J. Grollier – Neuromorphic computing: overview and challenges – YouTube –

Ocasys –

Mike Davies – New Tools for a New Era in Neuromorphic Computing – YouTube –

Joshua Yang: Memristive Materials and Devices for Neuromorphic Computing – YouTube –

Achieving Green AI with Energy-Efficient Deep Learning Using Neuromorphic Computing | July 2023 | Communications of the ACM –

Networking Neuromorphic Engineers – –

Training Spiking Neural Networks Using Lessons From Deep Learning:

10 minutes paper (episode 4); Spiking NN – YouTube –

Cosyne 2022 Tutorial on Spiking Neural Networks – Part 1/2 – YouTube –

Neuromorphic computing with memristors: from device to system – Professor Huaqiang Wu – YouTube –

Neuromorphic: BRAINLIKE Computers – YouTube –

ESWEEK 2021 Education – Spiking Neural Networks – YouTube –

Prof. J. Joshua Yang, Memristive Devices for Neuromorphic Computing – YouTube –

Online dynamical learning and sequence memory with neuromorphic nanowire networks | Nature Communications –

Neuromorphic learning, working memory, and metaplasticity in nanowire networks | Science Advances –

tinyML EMEA 2021 Tutorial: Bio-inspired neuromorphic circuits architectures – YouTube –

Leben und Elektronik – Was kann ein Memristor? – YouTube –

Approaching the Area of Neuromorphic Computing Circuit and System Design | IEEE Conference Publication | IEEE Xplore –

Memristor Discovery (Board, Chip, Software, Manual) – Knowm Inc –

DYNAP™-CNN: Neuromorphic Processor With 1M Spiking neurons –

Old 2010 article about memristor –

On-device machine learning with memristors in the neuromorphic era – YouTube –

Memristor-based Deep Spiking Neural Network with a Computing-In-Memory Architecture – YouTube –

A fully hardware-based memristive multilayer neural network | Science Advances –

Making Memristive Neural Network Accelerators Reliable –

Toolflow for the algorithm-hardware co-design of memristive ANN accelerators – ScienceDirect –

[REFAI Seminar 09/30/21] Circuit Design & Silicon Prototypes for Compute-in-Memory for Deep Learning – YouTube –

Novel Memristor-based Neural Network Accelerators for Space Applications | Activities Portal –

Electronics | Free Full-Text | A Unified and Open LTSPICE Memristor Model Library –


Colloquium: Memristive Neuromorphic Computing Beyond Moore’s Law – YouTube –

Neural Networks and Memristive Hardware Accelerators — Chair of Fundamentals of Electrical Engineering — TU Dresden –

Memristive devices based hardware for unlabeled data processing – IOPscience –

Memristor-based Spiking Neural
Networks.pdf –

21 March 2023 Overview of Neuromorphic Computing Challenges and Opportunities by Sakib Hasan PhD – YouTube –

Text Classification in Memristor-based Spiking
Neural Networks.pdf – and its code implementation on GitHub: text-classification-in-memristorsnn

Recent progresses of in-memory computing: materials, devices and architectures – YouTube –

Bill Dally – Trends in Deep Learning Hardware – YouTube –

Training Spiking Neural Networks Using Lessons From Deep Learning – YouTube –

Tutorial on snnTorch: Jason Eshraghian ICONS 2021 – YouTube –

Halide Perovskite Memristors for Neuromorphic Computing and Hardware Security – Rohit Abraham John – YouTube –

Roadmap on Neuromorphic Computing and Engineering – YouTube –

The Basics of Neuromorphic Computing – YouTube –

Spiking Neural Network – SNN – YouTube –

Sensors | Free Full-Text | Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities –

Chinese Scientists Develop Groundbreaking AI Chip Inspired by the Brain – YouTube –

Edge learning using a fully integrated neuro-inspired memristor chip | Science –

Neuromorphic computing with emerging memory devices – YouTube –

‘Mind-blowing’ IBM chip speeds up AI –

Deep Learning Hardware – YouTube –

tinyML Summit 2023: Tiny spiking AI for the sensor-edge – YouTube –

Low-Power Spiking Neural Network Processing Systems for Extreme-Edge Applications – Federico Corradi – YouTube –

In-Memory Computing based Machine Learning Accelerators: Opportunities and Challenges – YouTube –

Why spiking neural networks are important – Simon Thorpe, CERCO – YouTube –

Small Molecule Memristors for Neuromorphic Computing by Aaron Cookson –

Modeling and Demonstration of Hardware-based Deep Neural Network(DNN) using Memristor Crossbar Array – YouTube –

Memristors for Neuromorphic Circuits and Artificial Intelligence Applications –

Fundamentals of spiking neural networks and weight optimization –

Highly-Bespoke Robust Printed Neuromorphic Circuits –

Aging-Aware Training for Printed Neuromorphic Circuits –

Neuromorphic Circuits – A constructive approach –

Expressivity of Spiking Neural Networks –

European Solid-State Electronics Research Conference –

Neuromorphic Computing Group – Brain-Inspired Systems at UC Santa Cruz –

MemrisTec Summer School 2023 –

Neuromorphic Circuits and Systems: From Neuron Models to Integrate-and-Fire Arrays –

Neural Network Scalable Spiking Simulator (N2S3) –

Research Progress of Neural Synapses Based on Memristors –

Wei Lu (U Mich) Neuromorphic Computing Based on Memristive Materials and Devices –

Memristor Fabrication Through Printing Technologies: A Review –

Neuromorphic Computing with Memristors: From Devices to Integrated Systems –

Integrating dimensions to get more out of Moore’s Law and advance electronics –


The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks –

Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems –

Brain organoid reservoir computing for artificial intelligence –

Memristor-based spiking neural network with online reinforcement learning –

A Fully Memristive Spiking Neural Network with Unsupervised Learning –

Spiking Neural Network (SNN) With Memristor Synapses Having Non-linear Weight Update –

SNNSim: Investigation and Optimization of Large‐Scale Analog Spiking Neural Networks Based on Flash Memory Devices –

Memristor‐Based Neuromorphic Chips –

Lesson 2: The neuron and nervous system –

Purely self-rectifying memristor-based passive crossbar array for artificial neural network accelerators –

Fully hardware-implemented memristor convolutional neural network –

Efficient Structure Slimming for Spiking Neural Networks –

An overview memristor based hardware accelerators for deep neural network –

An integrate-and-fire neuron circuit made from printed organic field-effect transistors –

A Case for 3D Integrated System Design for Neuromorphic Computing & AI Applications –

Neuromorphic computing based on halide perovskites –

Memristors for Neuromorphic Circuits and Artificial Intelligence Applications –

NeuroPack: An Algorithm-level Python-based Simulator for Memristor-empowered Neuro-inspired Computing –

A pseudo-memcapacitive neurotransistor for spiking neural networks –

Revisiting the memristor concept within basic circuit theory –

Memristors Run AI Tasks at 1/800th Power –

Review on the Basic Circuit Elements and Memristor Interpretation: Analysis, Technology and Applications –

Hybrid 2D–CMOS microchips for memristive applications –

Three-dimensional integrated circuit using printed electronics –

CAREER: Enabling Brian-like Computing through 3D Neuromorphic Circuits and Systems –

Enabling Neuromorphic Computing for Artificial Intelligence with Hardware-Software Co-Design –

The improbable but highly appropriate marriage of 3D stacking and neuromorphic accelerators –

SkyNet: Memristor-based 3D IC for artificial neural networks –

Neuromorphic Hardware and Computing –

Modeling and Design of a 3D Interconnect Based Circuit Cell Formed with 3D SiP Techniques Mimicking Brain Neurons for Neuromorphic Computing Applications –

Modeling and Analysis of CMOS-based Folded Memristive Crossbar Array for 3D Neuromorphic Integrated Circuits –

Neuromorphic 3D Integrated Circuit: A Hybrid, Reliable and Energy Efficient Approach for Next Generation Computing –

Electrical modeling and analysis of sidewall roughness of through silicon vias in 3D integration –

A Novel Approach for using TSVs as Membrane Capacitance in Neuromorphic 3D IC –

Monolithic 3D neuromorphic computing system with hybrid CMOS and memristor-based synapses and neurons –

Electrical modeling and analysis of 3D synaptic array using vertical RRAM structure –

Design and simulation of memristor-based neural networks –

Research Progress of Neural Synapses Based on Memristors –

Inkjet-Printed High-Yield, Reconfigurable, and Recyclable Memristors on Paper –

Inkjet-Printed Tungsten Oxide Memristor Displaying Non-Volatile Memory and Neuromorphic Properties –

Artificial Neurons on Flexible Substrates: A Fully Printed Approach for Neuromorphic Sensing –

Python-Based Circuit Design for Fundamental Building Blocks of Spiking Neural Network –

CMOS Circuit Implementation of Spiking Neural Network for Pattern Recognition Using On-chip Unsupervised STDP Learning –

Circuit implementation of on-chip trainable spiking neural network using CMOS based memristive STDP synapses and LIF neurons –

Full CMOS Circuit for Brain-Inspired Associative Memory With On-Chip Trainable Memristive STDP Synapse –

Design and implementation of training in hardware for efficient artificial intelligence –

A Three-dimensional (3D) Memristive Spiking Neural Network (M-SNN) System –

A Novel Memristive Neural Network Circuit and Its Application in Character Recognition –

Memristor-Based Neural Network Implementation with Adjustable Synaptic Weights in LTSPICE –

A Memristor Neural Network Based on Simple Logarithmic-Sigmoidal Transfer Function with MOS Transistors –

SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning –

Programming memristor arrays with arbitrarily high precision for analog computing –

Nanodevices and Integrated Systems Laboratory at UMass Amherst –

Joshua Yang: Memristive Materials and Devices for Neuromorphic Computing –

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