News: Microelectronics
10 April 2026
QuInAs links device physics to AI system performance using ULTRARAM
ULTRARAM compound semiconductor memory technology developer QuInAs Technology Ltd of London, UK (which was spun off from Lancaster University in early 2023) has reported work that links device-level physics — including resonant tunnelling and floating-gate dynamics — directly to AI system performance through compact modelling and hardware-aware benchmarking, addressing a key limitation in how emerging memory technologies are typically evaluated. Published in Journal of Applied Physics, the paper ‘Artificial synapse based on ULTRARAM memory device for neuromorphic applications’ demonstrates how ULTRARAM can be modelled and evaluated as a synaptic memory element for next-generation AI hardware.
The company is also presenting this work on 10 April at 9:20am (Session 5A) at the International Symposium on Quality Electronic Design (ISQED 2026) in San Francisco, CA, USA (8–10 April). The presentation focuses on system integration and design considerations, bringing ULTRARAM into the electronic design automation (EDA) and system design community.
Picture: ULTRARAM 4x4 array for in-memory computing.
Based on III–V compound semiconductor heterostructures, ULTRARAM leverages quantum resonant tunnelling to enable energy-efficient, ultra-low-energy switching and long data retention. It combines the non-volatility of a data storage memory, such as flash, with the speed and endurance of a working memory, such as DRAM, while providing significantly improved energy efficiency. Target applications include artificial intelligence, quantum computing, space and defence.
Developed in collaboration with IIT Roorkee and Lancaster University, the reported work introduces a physics-based compact modelling framework that links device-level behaviour — including resonant tunnelling and floating-gate charge dynamics — to circuit- and system-level performance. This enables, for the first time, hardware-aware evaluation of ULTRARAM in neuromorphic and in-memory computing architectures, using crossbar array simulations and DNN+NeuroSim benchmarking on tasks such as CIFAR-10 classification.
“Much of today’s AI hardware research evaluates memory technologies under idealized assumptions,” says QuInAs’ CEO James Ashforth-Pook. “This work takes a different approach — connecting real device physics directly to system-level performance. That’s essential if we are to build practical, energy-efficient AI systems.”
The research shows that ULTRARAM can achieve competitive accuracy while offering advantages in energy efficiency and area compared to conventional SRAM-based approaches, highlighting its potential as a platform for future AI hardware.
“By integrating physics-based modelling with system-level benchmarking, we can better understand how emerging memory technologies behave in real AI workloads, rather than relying on idealized models,” says lead author Abhishek Kumar.
IQE and Quinas complete Innovate UK-funded £1.1m ULTRARAM industrialization project
