Automated analog circuit synthesis algorithms are conceptually different from manual design procedures mainly because of their limited learning capabilities, including understanding the causal justifications for certain design decisions. However, the causal information is critical for activities like constructing new circuit topologies, incrementally updating existing designs, circuit verification, and improving design optimization. This paper presents a model to represent the causal elements embedded in an analog circuit design and the methodology and algorithms to automatically extract the causal information. Experiments discuss the methodology for two state-of-the-art OpAmp/OTA circuits.

This paper presents InnovA, a cognitive architecture for creative problem solving in analog circuit design, e.g., topology creation (synthesis), incremental topology modification, and design knowledge identification and reuse. The architectural modules attempt to replicate cognitive human activities, like concept formation, comparison, and concept combination. The architecture uses multiple knowledge representations organized using topological similarity and causality information. Solutions are clustered, so that each cluster represents a specific set of performance tradeoffs, thus a fragment of the Pareto front in the solution space. New structural features are created through variation of existing features. New solutions are created by combining features from the same cluster, distinct clusters, and features that originate a new cluster. The related algorithms are discussed in this paper too. The architecture also incorporates modules mimicking the using of human emotions in memory formation and decision making, but these modules are still under development and are a main direction for our future work. This paper presents a number of examples to illustrate its using in various analog circuit design activities that are hard to be realized with traditional computational methods.

This paper describes three learning methods to acquire new insight and knowledge on how structural features relate to performance and constraints. Learning is at the feature-, concept-, and constraint-level. Experiments present the three learning types for a set of modern analog circuits and illustrate the using of the gained information for circuit synthesis.

This paper introduces a three stage low voltage, low power amplifier created by a design knowledge-intensive circuit topology synthesis methodology. The synthesis method flow begins with a set of starting ideas and then continues with a sequence of justified, causal design steps. Starting ideas combine physical and abstract features from either previous design or new insight. After combining the starting ideas, the related design sequence is identified step by step, so that each step either improves performance or relaxes design constraints. The created Opamp is a novel low voltage, low power circuit with better setting time (small signal characteristic) and slew rate (large signal characteristic). The proposed Opamp is realized in 0.2-μm CMOS technology. At 1-V supply voltage, it consumes less than 70μW static power. When driving a 5-pF capacitive load, the amplifier achieves 24MHz gain-bandwidth-product (GBW) and 5.1-V/μs average slew rate.

Mining engineering metaknowledge from electronic documents is important in devising new design solutions for emerging applications as well as in identifying new business opportunities. Continuously and comprehensively reviewing and assessing the entire technical literature to find the most relevant ideas and solutions is cumbersome as engineering innovations are constantly being proposed. In particular, high performance analog circuit design has been at the forefront of technological innovations given their importance in applications, like telecommunications, mobile applications, and health care. This paper proposes a model and mining techniques for representing analog circuit design metaknowledge. The metaknowledge representation includes three components: an associative component presenting the conceptual hierarchy of the considered circuits, a component expressing the performance capabilities (e.g., tradeoffs and bottlenecks) of the circuits, and a causal modeling part describing the more likely starting ideas and design plans used to create a circuit. Knowledge structures can be used to tackle new applications, identify opportunities to improve existing circuits, and validate design correctness. A case study illustrates the proposed knowledge structure for a set of 30 modern, high-frequency analog circuits.