Multi-level Co-Simulation of Mixed Technology Microsystems

The advent of highly integrated technologies such as MEMS, MOEMS, nanometer scale integrated circuits, mixed signal systems on a chip (SoC), and opto-electronic communication networks, requires flexible, sophisticated and efficient simulation tools. It is only through system level simulation based on behavioral models that efficient design space exploration can be performed across both architectural and implementation choices. In this paper, we present our solution based on a multi-level co-simulation environment for mixed domain micro and nano systems. I. BACKGROUND AND MOTIVATION Beyond electronic devices, new MEMs, NEMs, optical, chemical and fluidic devices are becoming available for integration into mixed technology systems. However, these novel devices lack the supporting model generation, simulation, design and fabrication infrastructure that has developed over the past decades for silicon integrated circuits. Therefore, integrating them into the existing highly abstracted silicon CMOS design flow is problematic. For example, writing SPICE or even VHDL/Verilog analog models for devices where one only has 3D FEM simulation data is not only painstaking and error prone, it also fails to yield flexible parametric models that could be used in optimization or prediction tools. The creation of simulation tools for multi-domain microsystems systems is challenging because these systems span the physical domains of electronics, photonics, and mechanics, as well as multiple orders of magnitude in both time and length scales. The difficulties are compounded by the fact that computational performance and accuracy are directly related to the level of detail in the underlying models. Multi-domain micro-systems are currently simulated by point tools using component level models performed at the physical, or device, level. Device level models focus on explicitly modeling the processes within the physical structure of a device such as electromagnetic fields, fluxes, mechanical stresses, and thermal gradients. These are typically described by partial differential equations in both space and time. Device-based tools are typically domain specific and computationally complex. In contrast, system-level tools are designed to include multiple domains and allow efficient system simulation, by modeling components by their functionality, or behavior, rather than their physical construction. On the other hand, behavioral level models capture the above, mentioned distributed effects in terms of parameters, and the models focus on the relationships between these parameters and state variables (e.g., optical intensity, phase, current, voltage, displacement, or temperature) as a set of temporal linear or nonlinear differential equations in both space and time. Therefore, one of the key requirements for integrating novel technologies into more traditional CAD flows is a methodology to extract abstract behavioral models from the lower level device models developed by the device designers. The second requirement is a robust simulation environment that can provide both simulation speed and fidelity. Meeting these two requirements will allow system designers to perform architectural design of novel technology for mainstream applications. The rest of this paper is organized as follows. First we discuss our modeling methodology. Then we give an overview of our system level simulation environment where these ideas are being tested. Then we present an example system, an RF switch, illustrating the advantages of this approach. II. MIXED SIGNAL MULTI-DOMAIN MODELING METHODOLOGY The proposed methodology for the simulation of multidomain, mixed signal systems can be described by three major tasks: system level description and modeling of the system, model abstraction of system components, and fast simulation algorithms. A. System Level Description and Modeling At the highest level, a system can be described as a set of component modules that are individually characterized and joined together by the mutual exchange of information. Each module processes some vector of input messages, updates its vector of internal state variables, and generates sets of output messages. In our approach, we use a piece-wise linear paradigm to represent the discrete event signals in the system. At the system level, signals are linearized and, consequently, the transfer function for the elements in the component subsystems can be obtained explicitly. Using a discrete event simulator, each module’s execution is based on the availability of new data values for its inputs. This allows each behavioral component model to be solved independently and consequently with the best possible strategy. B. Model Abstraction of System Components A state space description is typically used for the characterization of individual multi-domain components. A nodal description of the components is usually the final product of a discretization in time, space, or both of the physics based characterizations such as the ones used in Finite Element descriptions, EM analysis, or discrete and distributed electrical networks. A wide range of linear and non-linear dynamic behaviors can be mapped to a state representation: