Intelligent control of via formation by photosensitive BCB for MCM-L/D applications

Via formation is a critical process sequence in multichip module (MCM) manufacturing, as it greatly impacts yield, density, and reliability. To achieve low-cost manufacturing, modeling, optimization, and control of via formation are crucial. In this paper, a model-based supervisory control algorithm is developed and applied to reduce undesirable behavior resulting from various process disturbances. A series of designed experiments are used to characterize the via formation workcell (which consists of the spin coat, soft bake, expose, develop, cure, and plasma descum unit process steps). The output characteristics considered are film thickness, uniformity, film retention, and via yield. Sequential neural network process models are used for system identification, and hybrid genetic algorithms are applied to synthesize process recipes. Computer simulation results show excellent control of output response shift and drift, resulting in a reduction of process variation. The performance limits of the supervisory control system are investigated based on these simulation results. The control algorithm is verified experimentally, and the results show 82.6, 64.4, and 17.3% improvements in maintaining target via yield, film thickness, and film uniformity, respectively, as compared to open loop operation