Hardware-Efficient Analog Compressive Sensing IC for High-Density Neural Recording

Modern neuroscience and brain–machine interface research increasingly depend on high-density neural recording systems that capture activity from thousands of electrodes. These systems amplify, digitize, and transmit neural signals for analysis, yet growing channel counts expose hard constraints: enormous data bandwidth, strict power budgets to avoid tissue heating, and limited silicon area. Streaming raw data quickly becomes infeasible, and conventional compression schemes struggle to balance fidelity, latency, and energy efficiency. This motivates on-chip strategies that reduce data at the source without compromising the quality required for reliable spike detection. 

Imec is a world-leading research and innovation hub in nanoelectronics and digital technologies, leveraging its expertise in advanced semiconductor processes, system design, and AI to enable next-generation biomedical technologies, including neural interfacing and recording systems. The main goal of this PhD topic is to investigate and design a hardware‑efficient analog compressive sensing integrated circuit (IC). By exploiting the sparsity of extracellular spikes, the approach transforms high‑dimensional neural data into a low‑dimensional representation either before or during analog-to-digital conversion, significantly reducing bandwidth and energy per bit. Through co‑design of the analog front‑end with a signal‑combination stage that aggregates multiple channels into a reduced set of outputs, the system retains critical spike timing and amplitude information while minimizing silicon area and power consumption. The candidate will model and investigate various compressing methods and design innovative chip prototypes that could achieve more than 10× data‑rate reduction at iso‑accuracy compared to conventional neural recording chips, while ensuring robustness to multi-spike collisions and sensitivity to low-amplitude activity. The candidate will further contribute to the verification and electrical characterization of the prototypes under real-world biological conditions. 

Skills and background: