Variability-aware RRAM PDK for design based research on FPGA/neuro computing

The semiconductor industry has provided the devices we have enjoyed for many years, including mobile phones, personal computers, on-line banking etc. The growing functionality of these products is a result of making the components, namely transistors and memory elements, ever smaller, at the rate that in every 18 months or so the number of components in a given area has doubled, which also makes the devices run faster. The industry now runs into a fundamental roadblock in shrinking the devices further, so we need to look for a new device type which will continue to provide higher performance. One strong contender is the RRAM (resistive random access memory) which we will investigate in this project. This device can be programmed to offer either a high or low electrical resistance: that is, store a logic "0" or "1", or even with some intermediate levels in between. It can store information which will remain even after the power is turned off, as so called non-volatile.

With this device, a number of disruptive developments are under intensive research world-wide. Its first potential application is to increase the speed of the non-volatile memory chip in computers by more than 10 times and provide potential for further increase in the number of components. The second is in the artificial intelligence (AI) computing which mimics the functionality of human brains. AI has been widely used by Google, Facebook, Apple, etc. RRAM has the potential to bring a breakthrough in AI by solving the density, connectivity and memory bandwidth limitations of AI hardware based on conventional devices. The third is to revolutionise the programmable computing with its smaller size and non-volatility, providing advantages for computing in data centres and Internet of Things, in which the vast amount of data will be streamed through internet and the scalability and energy efficiency provided by RRAM become critical.

The behaviour of RRAM devices, however, is stochastic, meaning that a large variation occurs during the device operation. At present, the lack of systematic understanding of the variability and the missing tools for variability-aware simulation hinder the research progress in RRAM-based circuit and systems design for neuromorphic and programmable computing. In this project we will collaborate with UK's leading IC design company, ARM Holdings, and the world no.1 EDA software company, Synopsys, providing direct insight into the fundamental properties of RRAM variability and developing a predictive variability-aware product design kit (PDK) that can be directly used within commercial EDA software by designers, enabling the research and design of novel RRAM based neuromorphic and programmable computing systems. We expect this project to have a significant direct impact on the UK and global ICT industry in the forthcoming Artificial Intelligence (AI) and Internet of Things (IoT) era.

This project aims to address the critical simulation and characterisation of variability issues in resistive memory devices (RRAM), which place fundamental limits on the performance of its application in neuromorphic and programmable computing. The global IC market amounts to more than $335 billion in 2015. IC design plays an important part in the UK Electronic Systems Community that employs more than 850,000 people nationally in 30,000 enterprises, constituting 2.9% of the workforce, a vital enabler to wide market domains such as aerospace, defence, healthcare, retail, media and education. In this project we will collaborate with the leading company in the IC design markets, ARM and Synopsys, and we expect direct impact on the UK and global IC industry. Novel RRAM variability-aware product design kit will be developed in this project and disseminated into our industrial partners and other leading institute such as imec. Circuit/system design industry will benefit from this project by using the PDK as an enabler for innovative circuit design in neuromorphic and programmable computing. The predictive variability-aware model developed in this project should benefit circuit designs wherever RRAM is used, which allows systematic exploration of novel circuit topologies through simulation with commercial EDA tools. Detailed knowledge of device level variability issues in advanced manufacturing processes is now recognised as a key requirement for fab-less companies including those in the UK. The combination of theoretical modelling and characterisation used in this project will provide a test-proven approach in selection and evaluation of material, process and device structure, and provide useful guidance for the RRAM community in general. The research has the potential to put the UK at the forefront of a crucial emerging area in the coming AI and IoT era, which is likely to impact on the global economy as well as on the UK and European economy. The project has excellent potential to generate wealth in the UK by the worldwide sale of simulation tools and design IPs to the leading companies.