Semiconductor manufacturing test is affected by fabrication process and power supply voltage (PV) variation as demonstrated recently by the investigating team. Performing test using existing methods and without considering PV varaition will lead to defects being missed by during test leading to reduced yield and reliability of integrated circuits. This grant application is focused on exploring and developing new and efficient test methods capable of mitigating the impact of PV variation leading to improved test quality and higher dependability. This project will provide significant advances in the present state-of-the-art semiconductor test and will help to establish the scientific foundation required for the development of next generation PV variation-aware test methods and tools for nanoscale integrated circuits. This includes new fault models for resistive open and resistive short defects that capture PV variation; accurate metrics for assessing and quantifying the impact of such variation on the quality and cost of test, and two variation-aware test pattern generation methods (logic and delay) capable of mitigating test escapes due to PV variation and efficient in terms of defect coverage and volume of test data. The developed models, metrics, and test generation methods will be evaluated using comprehensive simulation with nano-meter synthesized benchmark circuits and real-life test problem provided by the project industrial partner. This is a three-year project involving one named post doctoral researcher and one PhD student. The project will be carried out in collaboration with ARM (Cambridge) and Synopsys (US), and in collaboration with Prof. K. Chakrabarty (Duke Uni.), and Prof. S. Kundu (Uni. of Massachusetts) as visiting researchers.The research we propose is aligned with the EPSRC signposted Grand Challenges in microelectronics design as identified by the EPSRC network grant Developing a Common Vision for UK Research in Microelectronic Design . This proposal is aligned in particular with GC3 (More for Less: Performance-driven design for next generation chip technology), where one of the main technical issues that need to be addressed in this GC is Test and Verification if the semiconductor industry is to continue to produce more efficient designs with better performance, lower power and lower test and verification cost.

Flash memories are used to store phone numbers, music, pictures and videos in mobile phones and are also frequently now used in place of magnetic hard disks in laptop computers. Such memories are non-volatile retaining information even if a battery looses all charge. Consumers constantly want more memory on their portable electronic devices to allow more video and music to be stored but flash memory is already close to the scaling limits preventing significant increases to memory sizes in the future. A flash memory consists of a floating gate charge node where the a single bit of digital information is stored as a "1" when the node is charged and "0" when the node is discharged. As the floating gate is reduced in size, there are more errors when electrons leak out of or onto the floating gate. These errors result from variation in floating gate size by just a few atomic layers which are sufficient to substantially change the applied voltage required to tunnel electrons onto or off the floating gate. This limit has been reached with present production. Our approach to improve flash memory and allow smaller memories is to use molecules which are produced chemically to allow charges to be stored as the digital memory and as the molecules are all identical, they do not suffer the same variability errors as the present silicon floating gate flash memories. Out ultimate aim is to use single molecules to enable further scaling thereby aiming to increase the amount of memory available in the future. We will also investigate molecules that can store more than "0" and "1" known as multi-valued memory. This multi-valued memory approach allows more bits to be stored on a single floating gate thereby allowing higher memory density expanding further what could be stored on a mobile phone or laptop computer. The approach we are taking requires the ability to measure the state an electron occupies on a single molecule. Therefore the technique developed here could be used to measure the properties of single molecules. This has potential applications for measuring the electronic properties of single molecules directly allowing the full characterisation of the molecular levels which at present is difficult to achieve. We believe these techniques can benefit a wide range of researchers in chemistry, physics, materials science and engineering in achieving far cheaper characterisation of materials at the nanoscale.

This fellowship programme will apply state-of-the-art 3D image processing and machine learning methods, developing them further where necessary, to deliver a new software tool that performs industrial production line 'virtual qualification' using part-specific simulations from 3D X-ray imaging in high-value manufacturing (HVM). Qualification is when manufactured parts are verified fit for purpose, often achieved by performing experimental tests representative of in-service conditions. Virtual qualification will verify by modelling micro-accurate digital replicas of the final part (flaws included) replacing costly and time-consuming experimental methods. Additionally, this will assess defects for performance impact (rather than expensive but unspecific pass/fail testing). The challenge is that image-based modelling currently requires significant human interaction over a timescale of weeks. Applying this to many parts takes significant time to complete unless methodology can be changed. The novelty of this proposal is to use machine learning with foreknowledge, due to production line parts being similar, to automate conversion of microresolution 3D images into part-specific models that simulate in-service conditions. This automation is required for the technique to scale for deployment in industrial manufacturing. Additionally, because much of the decision making entailed is subjective, and therefore prone to human error, a consequential benefit of automation is consistent outputs by removing this variability. This proposal focuses on image-based finite element methods (IBFEM), which merge real and virtual worlds to account for deviations caused by manufacturing processes not considered by design-based finite element methods (FEM), e.g. due to tolerancing or micro-defects. This implementation of part-specific modelling has applications in advanced manufacturing wherever there is variability from one component to another e.g. additive manufacturing or composites. A case study will be undertaken with the UK Atomic Energy Authority (UKAEA) for a heat exchange component. This will showcase the capabilities of the technique to automatically produce a report that estimates the impact of deviations from design on performance. Unlike FEM, which have undergone extensive certification and are industry-wide trusted methods, there has not been a systematic approach which can be used to benchmark image-based modelling workflows against verified experimental data. This work will produce benchmarks based on standards for experimental measurements of thermomechanical material properties to give confidence in the technique for industrial adoption. The database of benchmarks will be useful for those wishing to use image-based modelling to validate workflows and could contribute towards establishing new standards in the field. Central to this proposal is the use of FEM, the de-facto tool for predicting thermomechanical performance in engineering. Prof Zienkiewicz's research at Swansea University established it as a birthplace for FEM, and is now recognised as a leading research centre in the field. The team undertaking this fellowship, led by Dr Llion Evans, will be based at the Zienkiewicz Centre for Computational Engineering, Swansea University and will work in collaboration with the centre's head, Prof Nithiarasu, an expert in image-based modelling for biomechanics. Access to the equipment required for all aspects of this highly multidisciplinary work i.e. thermomechanical characterisation, 3D imaging and computing is available through complementary centres at the College of Engineering, Swansea University. To support this extremely multidisciplinary work, key industrial organisations will be collaborating on this project. Nikon Metrology Ltd. (X-ray imaging systems), Synopsys Inc. (image processing software), TWI (non-destructive testing and industrial standards), UKAEA (energy generation end-user) and Airbus (aerospace end-user).

Please see main (Glasgow) form

Please see form from lead site - Glasgow