UC Santa Barbara

The emergence of Large Language Models (LLMs) offers new opportunities for applying Machine Learning (ML) and Artificial Intelligence (AI) in semiconductor chip design and test (D&T). Realizing these opportunities requires a fundamentally different thinking from the past. For more than two decades, the semiconductor industry has been exploring applications of ML in D&T. Despite many promises, notable challenges remain in most of the application contexts.

The first part of the thesis (Chapter 2, 3 and 4) includes a review of works for applying ML in D&T, starting in 2003, and describes the journey leading to the current development of an AI Assistant called Intelligent Engineering Assistant (IEA). The journey evolved from one view to another, where each view perceived applying ML in D&T differently. In the first decade, the research took a data-driven view similar to that in common ML practices. This view was changed to a knowledge-driven view in 2014, due to the experience of solving a production yield problem for an automotive chip supplycompany. This experience is highlighted in the thesis, together with learning lessons from a variety of other works in the first decade.

The knowledge-driven view then lasted for four years. During the period, the research focused on finding ways to incorporate domain knowledge into the data learning process. It was in this period, the idea of Co-ML (complementary ML) first emerged. Co-ML formulates a ML problem as a decision problem where the outcome of the learning can be either a model (an answer) or no model (no answer). Then, in 2018 the idea of IEA, as an autonomous system, was first construed. This changed the knowledge-driven view to an autonomous system view.

In 2022, the autonomous system view was once again revised. It was realized that in order to achieve a practical IEA, one had to take a fundamentally different view from the past and perceived problem and solution as a pair, rather than perceived problem as given for finding a solution. We call this thinking the problem-solution dual view (Chapter 5).

Under our problem-solution dual view, applying ML in D&T is no longer seen as “static” in the sense that for a given problem, here is an ML tool for it. It is seen as a data exploration process, a search process driven by user, where each search step comprises a pair of problem instruction and problem solver. Consequently, there are two requirements for an IEA: (1) to provide a language for specifying problem instructions and (2) to provide a software platform capable of solving each acceptable problem instruction. This novel IEA thinking was realized in our first end-to-end IEA in 2022 (IEA-2022) inthe application context of wafermap analytics.

The development of IEA-2022 preceded the release of ChatGPT. At the time, IEA-2022 utilizes its predecessor GPT-3 model, only in a restricted way because of the limitations of the LLM. Then, the release of ChatGPT and its later models fundamentally changed design of IEA again (Chapter 6). In the latest IEA, called IEA-Plot, a knowledge graph (KG) is in place as the central piece to connect problem instruction to problem solver (Chapter 7). With a powerful LLM, the problem instruction can therefore be given in natural language. The instruction is then grounded by the KG internal to IEA in order to find a matching solution based upon a collection of solvers in the backend of IEA. IEA-Plot, again focusing on wafermap analytics, was demonstrated based on test data collected from several product lines. In the thesis, four chapters are devoted to discuss the development of IEA-Plot which is the central piece of this thesis work.

IEA-Plot is the first step moving forward to build a practical LLM-assisted AI Assistant in the semiconductor domain. There is one essential issue with the development of IEA-Plot: the construction of the KG. This motivated us to explore the possibility of using an LLM to assist the development of KG. Furthermore, in the current IEA the domain knowledge is stored explicitly in the KG. The LLM is used off-the-shelf. This raises the question whether or not it is possible to ingest the domain knowledge into an LLM and remove the dependency on KG. The last part (Chapter 8) of the thesis will touchbase on these two aspects. In particular, we present a novel idea called oracle-checker scheme (OC scheme) for utilizing an LLM by treating the LLM as an oracle. Findings for using LLM for KG development are summarized. Then, in the last Chapter 9 before the conclusion we paint a picture for how to ingest domain knowledge into an LLM by taking a generative AI approach.

While IEA-Plot is at the center of this thesis, it should not be seen as a standalone invention. IEA-Plot is a direct consequence from two decades of research on trying to apply ML in D&T. It exemplifies how to build an LLM-assist AI Assistant in practice. It marks the end of a two-decade journey and opens a new one toward generative AI.