Tutorials
T1: High-Speed PCB Design for Mixed-Signal Systems (MHz to GHz)
by Karim Bouzid (Université Laval, Québec City, Canada), PhD candidate.
June 21, 1:30 pm – 4:30 pm
T2: Electroanalytical microdevices: history, recent developments, and applications
by Raphael Trouillon (Polytechnique Montréal, Montréal, Canada).
June 21, 9:00 am – noon
T3: Building the Next Generation of Chips with Generative AI
by Sherief Reda, School of Engineering and Computer Science Dept, Brown University.
June 21, 9:00 am – noon
T4: Quantum Computing: Programming Fundamentals
by Algolab.
June 21, 9:00 am – noon & 1:30 pm – 4:30 pm
T1 : High-Speed PCB Design for Mixed-Signal Systems (MHz to GHz)
June 21 1:30 pm – 4:30 pm
by by Karim Bouzid (Université Laval, Québec City, Canada), PhD candidate.
— Abstract — This tutorial presents advanced concepts in high-speed PCB design for mixed-signal systems, with a focus on practical challenges encountered at frequencies ranging from MHz to GHz. It highlights how conventional circuit assumptions break down at high frequencies, where parasitic effects, electromagnetic propagation, and impedance discontinuities significantly impact system behaviour. The session introduces key principles, such as bandwidth versus physical trace length, transmission line effects, and impedance matching, along with an intuitive understanding of signal propagation in PCB structures. It also covers important design aspects, including reflections, crosstalk, and return current paths, emphasizing their role in signal integrity and electromagnetic compatibility. By combining theoretical insights with practical design rules and real-world examples, this tutorial provides participants
T2 : Electroanalytical microdevices: history, recent developments, and applications
June 21, 9:00 am – noon
by Raphael Trouillon (Polytechnique Montréal, Montréal, Canada).
— Abstract — This tutorial provides an overview of electroanalytical microdevices, covering their evolution, key technological advances, and a range of emerging applications. It introduces fundamental concepts before exploring current approaches in miniaturized electrochemical systems, including recent developments in device fabrication and sensing strategies. The session will also highlight how these technologies are being applied in biomedical and life science contexts, with selected examples illustrating their potential in areas such as diagnostics and neurochemical analysis. Combining foundational knowledge with insights into ongoing innovations, this tutorial offers participants a broad perspective on a rapidly evolving field.
T3 : Building the Next Generation of Chips with Generative AI
June 21, 9:00 am – noon
by Sherief Reda, School of Engineering and Computer Science Dept, Brown University.
— Abstract — The semiconductor industry faces a critical challenge: the cost and complexity of designing advanced System-on-Chips (SoCs) are increasing exponentially, creating an unsustainable productivity gap. As chip designs become more intricate, scaling up by adding more engineers or tools is no longer a viable solution. Generative AI (GenAI) presents a transformative approach to this problem by decoupling design complexity from manual human effort, dramatically increasing the productivity of individual engineers, and finding superior solutions to computationally intensive problems in chip design. This tutorial will provide an overview of the major research themes and latest results in the use of GenAI for digital chip design. A primary application of GenAI is the generation of synthesizable Verilog code from natural language descriptions using Large Language Models (LLMs). A critical challenge is achieving high synthesis quality. This is measured not just by whether the code works correctly but by how efficient the resulting circuit is in terms of Power, Performance, and Area (PPA). We provide a large-scale evaluation of LLM-generated Verilog quality using a wide range of models and designs. We show how techniques like Chain-of-Thought (CoT) prompting and fine tuning are used to guide the model’s reasoning, compelling it to break down problems logically, much like a human designer would. GenAI is also being deployed to address a significant bottleneck in modern chip design: verification. This process, which can consume up to 60% of a designer’s time, is being automated as LLMs learn to generate complex verification environments, testbenches, and formal properties (i.e., SystemVerilog Assertions) directly from design specifications. We will describe state-of-the-art methods for verification and show how to design LLM agents that can create testbenches in order to maximize functional coverage. Beyond code generation, GenAI is revolutionizing how engineers interact with vast and complex design data. We describe how Retrieval-Augmented Generation, frameworks like ChipXplore act as intelligent assistants. They allow engineers to ask natural language questions about massive datasets like Process Design Kits and design databases. The LLM-based agentic flow translates these questions into database queries, retrieving critical information faster and with fewer errors than manual methods, thereby dramatically improving productivity and accuracy. The tutorial will also describe how GenAI is used to create predictive tools and autonomous agents to automate the entire chip design flow. LLMs can analyze RTL code directly to estimate a circuit’s PPA metrics before synthesis, providing rapid feedback and enabling early design space exploration. We will show how GenAI techniques can create autonomous multi-agent systems that can manage the entire design workflow, from RTL to the final GDSII layout. In this paradigm, an LLM acts as a high-level reasoning engine, planning tasks, generating scripts for design automation tools, and executing the design flow. This agentic flow can continuously evaluate the results of each step and iteratively refine the design until all objectives are met. The tutorial will conclude with open research problems in the field, including the scarcity of high-quality public design data, the risk of hallucinations, the risk of IP violations, and the need to create multi-modal autonomous AI-driven agentic flows that can integrate textual specifications, structural code, and visual layout data to holistically optimize chip designs.
— Bio — Sherief Reda is a Full Professor at the School of Engineering and the Department of Computer Science, Brown University. He received his BSc (with Honors) and MSc in Electrical & Computer Engineering from Ain Shams University, Cairo, Egypt. He joined the School of Engineering at Brown University in 2006 after receiving his PhD in Computer Science & Engineering from the University of California, San Diego. His research interests are in the areas of energy-efficient computation, design automation, and embedded systems. He is an IEEE Fellow for his contributions to energy-efficient and approximate computing. Professor Reda has co-authored or edited two books and has over 150 articles in leading conferences and journals. Professor Reda has received several best paper awards. He has been a PI or co-PI on more than $22M in funded projects from federal agencies and industry corporations. Professor Reda is a holder of five US patents, some of which have been licensed and commercialized. He served as a technical program committee member for many IEEE/ACM conferences in his research area, and as an associate editor for Integration, the VLSI Journal (Elsevier) and IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD). Besides his academic work, he was a Principal Scientist at Amazon between 2021 and 2023, and he also serves as an expert witness in patent litigation lawsuits. He is currently an Amazon Scholar. He is a recipient of an NSF CAREER Award and is both an IEEE and an AAIA Fellow.
T4 : Quantum Computing: Programming Fundamentals
June 21, 9:00 am – noon & 1:30 pm – 4:30 pm
by Algolab.
— Abstract — This tutorial will provide a comprehensive introduction to quantum computing, designed for participants with little to no prior experience in the field. The morning session will focus on fundamental concepts, including the principles of quantum mechanics as applied to computation, qubits, superposition, entanglement, and basic quantum circuit models. In the afternoon, the tutorial will transition to a more practical and interactive format. Participants will be introduced to quantum programming frameworks and will explore simple implementations through guided examples and hands-on exercises. A more detailed outline will be provided in due course.
Since 2020, AlgoLab has been exploring the potential of quantum computing in a number of fields and boasts expertise that is unique in Quebec. The team works with companies, universities and government agencies on collaborative research projects in optimization, quantum machine learning and materials simulation.