Human-machine collaboration for improving semiconductor process development.

One of the bottlenecks to building semiconductor chips is the increasing cost required to develop chemical plasma processes that form the transistors and memory storage cells1,2. These processes are still developed manually using highly trained engineers searching for a combination of tool parameters that produces an acceptable result on the silicon wafer3. The challenge for computer algorithms is the availability of limited experimental data owing to the high cost of acquisition, making it difficult to form a predictive model with accuracy to the atomic scale. Here we study Bayesian optimization algorithms to investigate how artificial intelligence (AI) might decrease the cost of developing complex semiconductor chip processes. In particular, we create a controlled virtual process game to systematically benchmark the performance of humans and computers for the design of a semiconductor fabrication process. We find that human engineers excel in the early stages of development, whereas the algorithms are far more cost-efficient near the tight tolerances of the target. Furthermore, we show that a strategy using both human designers with high expertise and algorithms in a human first-computer last strategy can reduce the cost-to-target by half compared with only human designers. Finally, we highlight cultural challenges in partnering humans with computers that need to be addressed when introducing artificial intelligence in developing semiconductor processes.

Atomic Layer Etching: Rethinking the Art of Etch.

Atomic layer etching (ALE) is the most advanced etching technique in production today. In this Perspective, we describe ALE in comparison to long-standing conventional etching techniques, relating it to the underlying principles behind the ancient art of etching. Once considered too slow, we show how leveraging plasma has made ALE a thousand times faster than earlier approaches. While Si is the case study ALE material, prospects are better for strongly bound materials such as C, Ta, W, and Ru. Among the ALE advantages discussed, we introduce an ALE benefit with potentially broad application-the ALE smoothing effect-in which the surface flattens. Finally, regarding its well-established counterpart of atomic layer deposition (ALD), we discuss the combination of ALE and ALD in tackling real world challenges at sub-10 nm technology nodes.